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Open Access
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A&A
Volume 701, September 2025
Article Number A285
Number of page(s) 31
Section Catalogs and data
DOI https://doi.org/10.1051/0004-6361/202555485
Published online 26 September 2025

© The Authors 2025

Licence Creative CommonsOpen Access article, published by EDP Sciences, under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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1 Introduction

Asteroseismology from space-based photometric missions has, in the last decade and a half, heralded a revolution in our understanding of stars, exoplanet characterisation, Galactic archaeology, and many more fields within the broad landscape of astrophysics (Chaplin & Miglio 2013; García & Ballot 2019). This revolution was pioneered by the CoRoT (Convection, Rotation, and planetary Transits; Baglin et al. 2006; Michel et al. 2008) and Kepler/K2 missions (Borucki et al. 2010; Howell et al. 2014), and the field is now steadily maturing with the ongoing observations by the Transiting Exoplanet Survey Satellite (TESS; Ricker et al. 2014), which we are hopeful will continue for many years to come.

Although TESS, with its larger pixel size, shorter continuous observation periods, and higher background noise compared to, for example, Kepler, has not (yet) yielded the same specific breakthroughs in asteroseismology of main-sequence solar-like oscillators, it offers several unique advantages. TESS provides nearly full-sky coverage, enabling the study of a vast array of bright, well-characterised stars, and for specific regions of the sky, the total observing baseline has now surpassed the 4 years from Kepler. A 10-min observing cadence now enables asteroseismic studies of stars down to the subgiant regime based on full-frame images, and targeted 120-s and 20-s cadence observations have pushed the photometric asteroseismic studies into the K-dwarf regime (Hon et al. 2024).

The bright stars that can be uniquely observed by TESS are crucial because they serve as important benchmarks for future missions, such as PLATO (PLAnetary Transits and Oscillations of stars; Rauer et al. 2025), and many of them will be included in the target list for the future Habitable Worlds Observatory (HWO) mission (Mamajek & Stapelfeldt 2024). Moreover, their brightness makes them ideal for follow-up ground-based observations, which can significantly enhance our understanding of stellar physics.

With this work, we present a catalogue of 196 asteroseismic detections for the brightest solar-like main-sequence (MS) and subgiant (SG) oscillators observed by TESS. Hereafter, we refer to this cohort of stars as the ‘TESS Luminaries Sample’ (TLS). Of these, 128 are to our knowledge new detections, thereby more than doubling the number of known bright MS/SG oscillators, and include many of the stars predicted by Bedding et al. (1996) to show oscillation in the early days of asteroseismology. We have limited our sample to stars with V < 6, hence stars that, for most people, will be visible to the naked eye under favourable observing conditions. For MS/SG stars in the brightness range of interest here, asteroseismic analyses based on TESS data have to date mainly focused on single or only a few stars (see e.g. Ball et al. 2020, 2022; Metcalfe et al. 2020; Nielsen et al. 2020; Chontos et al. 2021; Huber et al. 2022) – with the TLS catalogue we aim to provide a comprehensive asteroseismic catalogue of all the bright solar-like oscillators observed by TESS, and we will continue to update and extend the catalogue as more data from TESS become available. With its focus on MS/SG stars, the TLS catalogue can be seen as a complement to the HD-TESS catalogue by Hon et al. (2022) focusing on bright evolved stars observed by TESS. The adopted brightness limit is admittedly somewhat arbitrary, but our analysis suggests that most fainter stars have already been covered by the recent impressive catalogues by Hatt et al. (2023) and Zhou et al. (2024), both of which provide detections for thousands of solar-like oscillators from TESS, but mainly focus on evolved solar-like stars (SGs and red giants). So, while we can generally confirm their detections for overlapping stars, we note that many of the brightest stars were not included in their analyses.

For the stars with detected oscillations, we provide measurements for the global asteroseismic parameters, Δν and νmax (Chaplin & Miglio 2013; García & Ballot 2019). With our results, we seek to highlight the opportunities for further detailed analysis from TESS for this cohort of stars that can prove valuable as benchmarks for stellar evolution theory and the asteroseismic method. Importantly, several of the stars that can be characterised asteroseismically by TESS, and in some cases by ground-based facilities such as SONG (Grundahl et al. 2017), will also be observed by the coming ESA PLATO mission and can here serve as key benchmarks for the quality of PLATO results (Sect. 5.3).

The brightness of the stars in this catalogue enables a detailed characterisation of their properties. This is both in terms of asteroseismology, with the current and future observation from TESS, and crucially also from ground-based observations, including, for example, spectroscopy (e.g., Tautvaišienė et al. 2022), interferometry (Vrard et al. 2024), spectropolarimetry (Metcalfe et al. 2023, 2024), measuring binary or multiple-star properties (Malla et al. 2024), and in some cases asteroseismic follow-up in radial velocity (Kjeldsen et al. 2025).

The paper is structured as follows: in Sect. 2 we outline the target sample, in Sect. 3 we describe our treatment of TESS data, while in Sect. 4 we describe our asteroseismic analysis methodology. Sect. 5 is devoted to presenting the results of our analysis and highlighting the potential use cases and interesting aspects of the stars in our sample, including synergies with PLATO and HWO, exoplanets, interferometry, binarity, solar analogues, and individual cases.

This paper is the first in a series, and follow-up papers in development will focus on the detailed peak-bagging and stellar modelling for the highest quality targets in the sample, a detailed analysis of the targets that overlap with PLATO fields, an analysis of the detectability of solar-like oscillators near the red-edge of the classical instability strip, updates to exoplanet parameters and analysis of HWO targets, and a detailed analysis of several individual stars or systems.

thumbnail Fig. 1

HR-diagram showing the criteria for our selection of targets. The red line shows our limits on MV and (B − V), and targets in the lower non-shaded region were selected for analysis. The marker colour indicates the V-band magnitude for the stars, while the marker size indicates the number of sectors TESS will have observed a given star in Cycle 6 (up to and including Sector 83). The stars in the top left shaded box are not expected to show solar-like oscillations and here we have not indicated V nor the number of sectors. The stars in the top-right shaded region contain evolved stars that may well show solar-like oscillations – these will be the subject of a future study.

2 Targets

The starting points for building our sample are the Hipparcos and Tycho catalogues (Perryman et al. 1997; Hoeg et al. 1997; van Leeuwen et al. 1997; ESA 1997). To limit our sample to stars visible to the naked eye, we first selected stars with a V-band magnitude below 6. Based on Hipparcos parallaxes, we computed the absolute V-band magnitude, MV, and used this in combination with the (B − V) colour as a proxy for the effective temperature to select stars that reside on the MS or SG branch – our specific selection criteria are shown in Fig. 1. The adopted limits are conservative because many of the high-MV targets are expected to be classical pulsators. However, we wanted to ensure that no solar-like oscillators were excluded near the red edge of the classical instability strip.

We also checked the Yale Bright Star Catalog (5th ed.; Hoffleit & Warren 1995) and the Gliese Catalogue of Nearby Stars (3rd ed.; Gliese & Jahreiß 1991) for targets that Hipparcos may have missed. However, none of the missing targets identified matched our selection criteria for MV and B − V. We note that for several targets that are members of binary systems, the Hipparcos (HIP) ID generally refers to the system rather than the individual components, as opposed to the TESS input catalogue (TIC; Stassun et al. 2019), which builds on Gaia (Gaia Collaboration 2018) and the Two Micron All Sky Survey (Cutri et al. 2003) – for these targets we manually assigned the TIC ID for the main component of the binary to the HIP ID.

Finally, we excluded known oscillators α Cen A and B (Bouchy & Carrier 2001, 2002; Carrier & Bourban 2003; Kjeldsen et al. 2005), and α CMi (Procyon; Brown et al. 1991; Martić et al. 1999; Arentoft et al. 2008) from our analysis as these extremely bright targets require special treatment in the form of a recalibration of the target pixel files and the smear data1.

From data availability, our sample is limited to stars with 120-s or 20-s cadence observations in at least one TESS sector up to and including Sector 77. These criteria leave us with 1060 stars to be analysed (see Fig. 1), and of these 311 have at least one sector with 20-s cadence observations.

By the end of TESS Cycle 7 (up to and including Sector 92) an additional 69 targets that match our MV and B − V selection criteria will have been observed (the observing cadence will depend on their inclusion in Guest Investigator proposals), among which are well-known asteroseismic targets such as 18 Sco (Bazot et al. 2011) and 70 Oph (Carrier & Eggenberger 2006b). These targets are primarily located near the ecliptic plane in the constellations of Libra, Scorpius, Ophiuchus, and Sagittarius, and none of them overlap with the PLATO fields (Sect. 5.3). These targets will generally only be observed during a single sector and, in a few cases, two to three sectors, so for the most promising of these targets, having access to 20-s cadence observations will be particularly important. Finally, 19 Hipparcos targets that match our MV and B − V criteria have not been, and will not be, observed by TESS up to and including TESS Cycle 7. Table A.1 in Appendix A provides an overview of these targets.

3 Data

Our main source of data comes in the form of PDCSAP light curves generated by the TESS Science Processing Operations Center (SPOC; Jenkins et al. 2016). We used light curves with both a 120-s and, when available, 20-s cadence (introduced from Sector 27 onwards). In sectors where both 20- and 120-s light curves were available, we also used the 20-s data binned to a 120-s cadence. As demonstrated by Huber et al. (2022), the 20-s data from TESS generally has lower noise than 120-s data, especially for bright stars, and with a reduction of scatter of the order ~25% at TESS magnitude 6 (see Sect. 5.2). All data were downloaded from the Mikulski Archive for Space Telescopes (MAST) using functionalities provided by Astroquery (Ginsburg et al. 2019) and lightkurve (Lightkurve Collaboration 2018).

For the targets where a detection of oscillations was made from an initial inspection of the data, and/or where a detection was expected2 from the asteroseismic target list (ATL; Schofield et al. 2019) in the updated version provided by Hey et al. (2024)3, we also analysed the data contained in the star’s target pixel files (TPFs; see Sect. 4). In the TPF analysis, we specifically focused on the light curve improvement from constructing custom apertures. We used the K2P2 pipeline (Lund et al. 2015) to create apertures via the density-based clustering algorithm DBSCAN, as implemented in scikit-image (van der Walt et al. 2014), combined with a watershed algorithm to separate close targets. The clustering algorithm was applied to pixels with flux values greater than 3 times the standardised median absolute deviation (MAD) from the median flux level. This routine is, by construction, excellent at defining apertures that include pixels that are grouped, but the occasional long bleed trails from the blooming of brighter saturated stars are more difficult to handle. However, while the apertures defined by SPOC are often small near the centroid of the star, the bleed trails are generally well-covered. Therefore, in the end, we opted to construct apertures by combining those from K2P2 and SPOC.

In certain instances, we could only make a positive seismic detection after adopting the custom joint aperture and, for several cases, the custom apertures significantly improved the data quality. Fig. 2 provides an example of a problematic SPOC aperture for the star ψ1 Dra A (TIC 441804568). The scatter in the light curve introduced by the SPOC aperture, which is missing several bright pixels, inhibits the detection of a seismic signal. As seen, when adopting the larger custom aperture, the noise level in the power spectral density (PSD) is significantly reduced, and oscillations can be detected.

We noticed that the pixel stamp available for 20-s cadence data was sometimes significantly smaller than the corresponding 120-s cadence pixel stamp – examples of this are δ Eri (TIC 38511251), γ Lep (TIC 93280676), and ϵ Eri (TIC 118572803). For these stars, the saturated bleed trails extend beyond the 20-s cadence stamp, causing the resulting light curves to have high noise levels. This inhibits the detection of oscillations from the 20-s data, while detections from the 120-s data are possible.

In some cases, mainly for the brightest stars in the sample (e.g. θ UMa; TIC 150226696), SPOC light curves are not available for all sectors. In these cases, TPF data are needed to make full use of the TESS observations. We refer to Appendix B for additional details on the comparison of SPOC and custom apertures.

Before searching for oscillations, we processed the light curves using the KASOC filter (Handberg & Lund 2014) to correct for any spurious signals or long-term trends (see, e.g., Campante et al. 2019; Jiang et al. 2020; Ong et al. 2021). In this filtering and the construction of new apertures, we followed the suggestion by Huber et al. (2022) to adopt the default quality bitmask defined by lightkurve4 for 120-s cadence data and the hard bitmask for 20-s cadence data.

4 Asteroseismic analysis

To identify targets of interest for asteroseismic analysis, an initial inspection of SPOC and TPF data (Sect. 3) was conducted using a variety of methods, including visual inspection of all data, autocorrelation function (ACF) analysis of the PSD, échelle diagrams, comparison to expectations from the ATL, etc. To obtain the first estimates of the global asteroseismic parameter νmax (the frequency of maximum oscillation power), we used a 2D-ACF method (see Verner & Roxburgh 2011), as implemented in lightkurve.

For stars with detected oscillations (or an expected detection of oscillations from the ATL), we used the pySYD pipeline (Chontos et al. 2022) to extract the global asteroseismic parameters Δν and νmax. The pySYD pipeline is an open-source adaptation of the closed-sourced SYD pipeline (Huber et al. 2009), which was extensively tested and benchmarked to Kepler LEGACY results (Lund et al. 2017). To summarise, pySYD first performs an automated optimisation to identify the best-fit background model due to stellar granulation on different timescales and with varying amplitudes, which can ultimately bias parameter estimates if not properly accounted for. The best-fit background model is then subtracted from the PSD to calculate a background-corrected power spectrum (BCPS), from which νmax and Δν can be measured. The frequency corresponding to maximum power (νmax) is adopted as the frequency with peak power in the heavily smoothed BCPS. An autocorrelation function is then used to identify the characteristic frequency spacing (Δν), which corresponds to the average frequency separating modes of the same spherical degree (l) and consecutive radial order (n). Due to highly correlated data and the stochastic nature of solar-like oscillations, parameter uncertainties are estimated through a bootstrapping technique discussed in more detail in Huber et al. (2009).

As mentioned in Sect. 3, a detection of oscillations is not always possible from all data products, either because of problems with the aperture and systematics in the light curve or simply from too high noise levels from 120-s cadence data compared to 20-s cadence data. To this end, pySYD was applied to PSD prepared from each of the SPOC and custom aperture light curves. We tested on both 120-s cadence and, if available, 20-s cadence data. We calculated a variance-weighted PSD from the full light curves, as well as a PSD from the weighted PSD of individual sectors (weighted by the average variance in each sector). From the results obtained from the different data sets, we identified the ones that agreed within 3 standardised MAD of the median of all results in both νmax and Δν, checked that these agreed within errors with the initial assessment of νmax, and that Δν and νmax were in correspondence with the known relation between these parameters (Hekker et al. 2009; Stello et al. 2009; Huber et al. 2011). From the measurements meeting these requirements, we computed the variance-weighted averages of Δν and νmax as our final reported parameters; see Sect. 5.1 for details.

As an additional validation of the identified detections, we also applied the A2Z pipeline (Mathur et al. 2010b) to our data. Briefly, A2Z first blindly searches the modes by computing the power spectrum of the power spectrum in sliding boxes along the PSD. This allows us to measure Δν along with the frequency range where the modes are detected. The background is then fitted with one Harvey model (Harvey 1985) for the granulation and a component for the photon noise. After subtracting the background, a Gaussian function is fitted around the frequency range found in the first step to obtain νmax. For cases with a low signal-to-noise ratio, the search for the modes was forced to the expected νmax. The 196 stars in the sample represent the consolidated cohort of stars analysed.

thumbnail Fig. 2

Example of the effect of adopting custom apertures, here for ψ1 Dra A (TIC 441804568) as observed during Sector 15 in 120-s cadence, where the SPOC aperture is missing several high-flux pixels. Similar apertures are seen for ψ1 Dra A in other sectors. Left: adopted aperture, shown with a black outline, combining the SPOC aperture, in orange, and the K2P2 aperture, in red. The green outline shows the aperture used to estimate the background. In blue, the median pixel flux levels are shown on a log-scale. Right: segments of the power-density spectra of the filtered SAP light curves from the apertures on the left, where the PSD obtained from the SPOC aperture is shown in orange and the one from the adopted custom aperture is shown in red. The inset shows a zoom of the region with identified oscillations from the custom aperture data. The small inset to the right shows the échelle diagram of the zoomed region after correcting for the background using a robust Siegel slope estimator.

5 Results

From our asteroseismic analysis, we detected oscillations in a total of 196 stars and provide the extracted global asteroseismic parameters Δν and νmax in Table D.1. Examples of PSD for some targets with detected oscillations, many of which are new detections, are shown in Fig. 3. In Fig. 4, the TLS is compared to the cohorts obtained from ground-based efforts, other studies based on TESS data, and the previous missions of CoRoT, Kepler, and K2.

As seen from Fig. 4, the main overlap with the TLS is from oscillators identified in ground-based efforts, and partly from the Hatt et al. (2023) and Zhou et al. (2024) samples, where our new detections extend these works to brighter stars. In terms of proximity, and thereby brightness, we see that the oscillators found from TESS are complementary to those found from Kepler, with K2 and CoRoT nearly closing the gap, at least for SGs and more evolved stars. Consequently, the overlap with these missions is very limited, and only includes the CoRoT target HD 49933 (Mosser et al. 2005; Appourchaux et al. 2008; García et al. 2010) and the two Kepler targets θ Cyg (Guzik et al. 2016) and 16 Cyg A (Metcalfe et al. 2012). With continued observations from TESS, the fainter magnitude limit for seismic detections on the MS will further close the gaps between the samples (Campante et al. 2016). Although we did not investigate the seismic detectability in stars fainter than V = 6 in this study, a comparison with the extensive Hatt et al. (2023) and Zhou et al. (2024) catalogues suggests that very few additional MS targets would be detected from the current amount of data. All TLS stars with an earlier seismic detection are identified in Table D.1, with reference given to the discovery paper.

thumbnail Fig. 3

Examples of PSD for a small subset of stars with detected oscillations, arranged according to increasing νmax (see Table D.1 for details). The spectra have been smoothed by an Epanechnikov kernel (Epanechnikov 1969) with a width of Δν/20.

5.1 Asteroseismic parameters

The correlation between the extracted Δν and νmax values is shown in Fig. 5, together with the expected empirical relation obtained from Kepler by Huber et al. (2011). The measured values are generally well within expectations, indicated by the 1- and 2-σ bands to the empirical relation. The right panel of Fig. 5 shows the kernel density estimates of the relative Δν and νmax uncertainties, with median values of ~1.6% in Δν and ~3.7% in νmax. These typical uncertainties are comparable to similar analyses in the literature of MS/SG stars observed with Kepler and K2 (Verner et al. 2011; Viani et al. 2019; Lund et al. 2024; Sayeed et al. 2025). From our analysis, we find that the typical RMS deviation amongst the different values used in the reported weighted average is well below the typical uncertainties, at ~0.5% in Δν and ~1.4% in νmax. Notably, we also find no indications of systematic biases between the different analyses.

We note that the uncertainties obtained for Δν and νmax from the individual data products are generally lower (~0.8% in Δν and ~2% in νmax) for 20-s cadence data (when available) or the combination of 120-s and binned 20-s data (depending on the amount of 20-s cadence data available). While these data naturally contribute proportionally more to the final value in the weighted average, the uncertainties are increased slightly by the inclusion of 120-s data and the averaged PSD. However, considering that our main focus is the detection of oscillations and given that not all stars have 20-s cadence data available, we opted to include these data products in the reported averages. Indeed, in some cases the detection and measurement of Δν and νmax were only possible when using the averaged PSD (like for 19 Dra (h Dra/TIC 289622310) and o2 Eri (TIC 67772871; Sect. 5.9)).

In addition to the global asteroseismic parameters, we estimate5 that ~63% (or ~125 stars) will be amenable to peak-bagging and the extraction of individual mode parameters (e.g., Handberg & Campante 2011; Davies et al. 2016; Lund et al. 2017; Corsaro et al. 2020; Nielsen et al. 2021). Such a peak-bagging effort and modelling of the asteroseismic parameters will be the subject of future work.

Comparison to the literature

As a check of our global asteroseismic parameters, we compared our values to those of Hatt et al. (2023), Zhou et al. (2024), and Corsaro et al. (2024) for the stars we have in common. In addition, we also compared to the predicted values from the ATL (which was included in the initial pruning of our sample), both versions 1 (Schofield et al. 2019) and 3 (Hey et al. 2024).

Our comparison for νmax is given in Fig. 6, and shows good overall agreement with the other studies using TESS data. The differences are generally within uncertainties, and we see no apparent bias in differences against νmax. We find the ATL typically underestimates νmax compared to our values, at a level of ~10% for ATL1 and ~16% for ATL3. For ATL3, we can trace this to a general offset in the log g and Teff values adopted from Gaia DR3 (Gaia Collaboration 2023). We refer to Sect. 5.2 and Appendix C for further discussion on the ATL comparison, which should be considered in future target selection efforts.

In terms of our ability to detect oscillations, we note that for all stars in the Hatt et al. (2023) and Zhou et al. (2024) catalogues that overlap with our sample, we also obtain a detection. Indeed, we confirm all previous detections from single-star analyses of TESS data that we could find in the literature, except for two stars. These are HD 4628 (HIP 3765) and 111 Tau (HIP 25278), listed as confident detections in Corsaro et al. (2024) from TESS observations, for which we could not confirm a detection in our analysis.

Among the ten stars flagged as having moderate, weak, or inconclusive seismic detections by Corsaro et al. (2024, their Table 2) that overlap our sample, we obtain clear detections for η Cas, 47 UMa, HD 30562, and HD 9562, thereby corroborating the seismic nature suggested by those authors. Interestingly, these four cases all have negative 1 n-Bayes factors from the Bayesian model comparison in Corsaro et al. (2024), suggesting a preference for the null hypothesis with no seismic power excess.

Among stars with ground-based detections, we have identified only seven matching our selection criteria where a positive detection could not be made, namely 18 Sco (HIP 79672), 70 Oph (HIP 88601), τ Boo (HIP 67275), ϵ Indi (HIP 108870), τ Cet (HIP 8102), ι Hor (HIP 12653), and HD 219134 (HIP 114622). No TESS observations up to Sector 77, as considered in our analysis, are available for 18 Sco (Bazot et al. 2011) and 70 Oph (Carrier & Eggenberger 2006b), but both will be observed for one sector during TESS Cycle 7 (Table A.1). For τ Boo (Borsa et al. 2015) data are available from two sectors, although only with a 120-s cadence; ϵ Ind (Campante et al. 2024; Lundkvist et al. 2024) has data from two sectors, including one of 20-s cadence, and with more observations scheduled for Sector 95; τ Cet (Teixeira et al. 2009) also has data from two sectors, one with 20-s cadence, but unfortunately the 20-s cadence stamp is too small to contain the star’s saturation trails, which adds significant noise to the photometry; ι Hor (Vauclair et al. 2008) has five sectors of data, of which three are of 20-s cadence; HD 219134 (Li et al. 2025) has four sectors of data, of which two are of 20-s cadence, and with additional observations scheduled for Sector 84. Of these five non-detections, ϵ Ind, τ Cet, and HD 219134 are cool dwarfs (spectral types G8, K5, and K3, respectively) and therefore have very low oscillation amplitudes (Kjeldsen & Bedding 1995; Corsaro et al. 2013).

thumbnail Fig. 4

Comparison of the TLS with solar-like detections in MS/SG stars from other missions. Left: position of solar-like oscillators in the HR-diagram, with an indication of the selection criteria in MV and (B − V) used to define our sample (Sect. 2). The marker size indicates the V-band magnitude of the stars, while the marker edge colour indicates how or by which mission oscillations were first detected. Any stars with a detection of oscillations from this work are shown with a filled yellow marker. Stars with ground-based detections were identified from individual cases in the literature (see Table D.1 and Sect. 5.1); the Kepler comparison sample was constructed from the compilations of Lund et al. (2017), Serenelli et al. (2017), Mathur et al. (2022), in addition to Kepler-444 (Campante et al. 2015) and θ Cyg (Guzik et al. 2016); the nine stars from CoRoT were identified from individual cases in the literature (Barban et al. 2009, 2013; Appourchaux et al. 2008; Mosser et al. 2009; Mathur et al. 2010a, 2013; Ballot et al. 2011; Boumier et al. 2014; Castro et al. 2021); the stars forming the K2 sample are obtained from Lund et al. (2016, 2024); while the TESS sample was obtained from the catalogues of Hatt et al. (2023), Zhou et al. (2024), and Corsaro et al. (2024, considering only their confident detections; their Table 1), in addition to individual cases from the literature (see Table D.1). For the TESS and K2 comparison samples, we have limited these to stars with νmax < 284 μHz. Right: distribution of the stars in terms of distance and νmax, using only stars that in the left plot fall within the MV and (B − V) boundaries defined in our target selection. We note that α Cen A+B, at a distance of ~1.35 pc, have been omitted from the plot. Distances and magnitudes used in this plot were adopted from the TESS Input Catalog (TICv8.2; Paegert et al. 2021). The horizontal dashed line indicates the solar νmax for comparison.
thumbnail Fig. 5

Left: correlation between the measured global asteroseismic parameters Δν and νmax. The dashed line indicates the empirical relation from Huber et al. (2011) together with the 1- and 2-σ confidence bands on their relation. Right: KDE of the relative uncertainties on Δν and νmax for the sample, with median values of ~1.6% in Δν and ~3.7% in νmax. The ticks at the bottom of the panel indicate the individual values, coloured according to the legend.
thumbnail Fig. 6

Comparison of νmax values for the stars in our sample that overlap with those of Hatt et al. (2023), Zhou et al. (2024), and Corsaro et al. (2024, considering only their confident detections; their Table 1), with the predictions from the Asteroseismic Target List (ATL) versions 1 (Schofield et al. 2019) and 3 (Hey et al. 2024). Left: direct comparison between our values and those published in the literature or predicted in the ATLs. The colour indicates the comparison source (see legend), while the numbers in the legend indicate how many stars are in common with the different comparison sources. Middle: relative differences between the values. Values beyond either +30% or −60% have been adjusted to these values (dotted lines) for a better visual rendition. Right: KDE of the relative differences.

5.2 Detectability and noise properties

From our asteroseismic analysis, we find that our two sources of light curves, those from SPOC and those extracted from TPFs using custom apertures (Sect. 3), greatly complement each other. Generally, the photometric quality is comparable, but with some outliers and with ranges in visual magnitude where the custom data performs better, typically from an improved aperture that better conserves the stellar flux. As an example of this, Fig. 7 provides a comparison between 20-s cadence light curves (including all stars where custom apertures were computed) from SPOC and those from custom apertures. The two metrics shown are the 1-hour root-mean-square deviation (RMSD; calculated as the standardised MAD of the data binned to 1-hour) and the point-to-point (P2P) median difference variability (MDV; calculated as the median of the absolute point-to-point flux differences, binned to 120-s cadence), both plotted as a function of TESS magnitude. The 1-hour RMSD is similar to the metric estimated in Huber et al. (2022) (their Fig. 2b), and we find that the levels from our sample agree well with those from Huber et al. (2022) in the magnitude range around Tmag ~5.5 where the samples overlap. The P2P-MDV metric is included to measure the high-frequency noise, which is particularly important for the detection of oscillations in MS/SG stars.

We confirm the lower noise of 20-s versus 120-s cadence data, as demonstrated by Huber et al. (2022), and we can extend their reported improvement of 20–30% to stars down to Tmag ~4.3. Brighter than this Tmag, we observe less improvement, but note that the sample size in this regime is limited to only 39 stars with data from both cadences. For the high-frequency noise, captured by the PTP-MDV, the improvement seems to be even greater, at a level of 30–40% in the median and remaining nearly constant across the magnitude range covered by our sample.

In Fig. 8, we show the minimum high-frequency noise levels (median between 3000 μHz < ν < 4000 μHz) from the power density spectra considered in our analysis against the measured (blue) or predicted (green) νmax for the stars with a seismic detection or a detection probability in ATL3 of Pdet ≥ 50% (based on whichever is highest if both 20-s and 120-s cadence observations are available). As expected, we see that stars with a positive seismic detection at a given νmax generally have lower noise levels than stars without a detection. If we look at the ATL3 detection probabilities, we see that only a single star (HD 49933) with a firm detection has a detection probability below Pdet ~80%, and the vast majority has a rounded probability of 100%. As noted in Sect. 5.1 the ATL3 νmax values are generally underestimated, and hence Pdet is overestimated, suggesting that most of the stars without a detection, but with high predicted detection probabilities, may indeed be overestimated. We also see that the stars with confirmed detections, but detection probabilities below 100% generally have slightly overestimated νmax values from ATL3 (hence underestimated detection probabilities). We refer again to Appendix C for further details on the ATL comparison. It is also worth noting that the ATL detectability calculation does not take into account that some targets could have an increased activity level, which is known to suppress the amplitudes of oscillation modes (García et al. 2010; Chaplin et al. 2011a; Bonanno et al. 2014; Mathur et al. 2019; Sayeed et al. 2025). This could also explain why oscillations are not detected in several stars where the noise level should be low enough for detection.

5.3 TLS and PLATO

Because they are – or have the potential to become – extremely well characterised, the stars of the TLS are of particular interest as calibrators and benchmarks for future asteroseismology missions, such as the ESA PLATO mission scheduled for launch in late 2026 (Rauer et al. 2025). PLATO’s planned observing strategy is currently focused on two so-called “Long-duration Observation Phase” (LOP) fields, each of which will cover a 49° × 49° region of the sky (Rauer et al. 2025). While subject to potential changes following the evaluation of initial observations, the nominal plan is to conduct observations for each of the LOP fields for two years. The 24 “normal” cameras will observe at a cadence of 25 s, while a cadence of 2.5 s will be used for the two “fast” cameras, capable of observing towards the centre of the FOV. At the time of writing, only the southern LOP field (LOPS2; centred on l = 255.9375°, b = −24.62432° in Galactic coordinates) is fully defined6 and this is where observations are scheduled to start (Nascimbeni et al. 2025). We provisionally adopt the northern LOP candidate field LOPN1 (l = 81.56250°, b = 24.62432°) as defined by Nascimbeni et al. (2022). Until the PLATO Science Working Team (PSWT) makes its final field selection, however, there is no assurance that LOPN1 – or any other northern variant – will ultimately be observed.

In Fig. 9, we show the sky distribution of the TLS, including the overlap with the PLATO LOP fields7. Table D.2 provides an overview of the targets within the current LOP field definitions, and also those near the fields (defined as being within 5° of the field edges). To check if a given target will be within the LOP fields, we tested against the field edges obtained from the LOP field versions pLOPN1PIC2.0.0.1-t and pLOPSsPIC2.0.0.1-t of the PLATO Input Catalogue (PIC; Montalto et al. 2021). We found that 10 targets are within the LOPS2 field, while 24 targets are within the current LOPN1 field. Interestingly, we identified several targets as spectroscopic and/or visual/astrometric binaries, where independent constraints can be placed on the stellar masses (see Sect. 5.7, Table D.5) and, in some cases, also on radii from interferometry (see Sect. 5.6, Table D.4).

Based on the brightness of our sample and our target selection strategy (Sect. 2), most (if not all) of the stars overlapping the PLATO fields should meet the requirements for the P2 “bright-star” sample (Montalto et al. 2021; Rauer et al. 2025; Goupil et al. 2024). We note, however, that the stars will generally be best suited for observations with the two fast cameras, whose dynamic range is 4 < V < 8. For the normal cameras, optimised for stars fainter than V = 8, it remains to be seen how well photometric observations can be extracted for heavily saturated stars, for example, from extended imagettes, which will experience significant blooming (e.g., Jannsen et al. 2024). Interestingly, observing TLS targets with the fast cameras will deliver simultaneous blue (505–700 nm) and red (665–1000 nm) photometry. Access to these two passbands could provide new insights on mode physics and on the links between asteroseismic observables and fundamental or dynamical stellar properties, for example, rotation, convection, and magnetic activity (e.g., Houdek & Dupret 2015; Santos et al. 2019; Sreenivas et al. 2025).

It would be instructive for the stars that, in addition to now being known asteroseismically, are well-characterised binaries, to be considered for the science calibration and validation PIC (scvPIC; Gaulme 2023) and possibly for the so-called “prime” sample of stars that will receive the highest priority throughout the mission (Rauer et al. 2025). The TLS should be taken into account when or if the PSWT finalises a Northern LOP field. In the provisional northern layout, TLS could provide a crucial addition to the sample of stars where stellar magnetic activity can be studied using asteroseismology (e.g., García et al. 2010; Chaplin & Basu 2014; Salabert et al. 2016; Kiefer et al. 2017; Santos et al. 2018) – particularly valuable because most ground-based activity surveys (Mount Wilson, Lowell SSS, TIGRE, STELLA, etc.; see, e.g., Jeffers et al. 2023) also operate in the northern hemisphere. Furthermore, μ1 Her (HIP 86974), monitored for more than 10 years in radial velocity by SONG (Grundahl et al. 2017) and on track to become one of the best characterised benchmark asteroseismic SGs, lies near (i.e., ≤ 5° away from) the current LOPN1 field edge. A dedicated indepth analysis of the TLS stars in or near the PLATO fields will be the subject of subsequent analysis (Panetier et al., in prep.). We also refer the reader to Eschen et al. (2024) and Nascimbeni et al. (2025) for additional analysis of the target content of the PLATO LOP2S field.

thumbnail Fig. 7

Comparison of noise statistics between different light curve sources and observing cadences, including all stars with 20-s cadence observations where a custom aperture was constructed (cf. Sect. 3). Top left: RMSD of 20-s light curve flux binned to 1 hour against TESS magnitude for both SPOC and custom aperture data. Top right: P2P-MDV of 20-s light curve flux binned to 120-s against TESS magnitude. Bottom left: ratio between the 1-hour RMSD from 20-s and 120-s cadence custom aperture data. The red markers indicate median-binned values, with uncertainties given by the standardised MAD, while the green markers give the ratios provided by Huber et al. (2022) (their Table 1). Bottom right: ratio between P2P-MDV from 20-s (binned to 120-s) and 120-s cadence custom aperture data. Red markers again indicate median-binned values, with uncertainties given by the standardised MAD.
thumbnail Fig. 8

Comparison of noise levels and predicted detectability in ATL3 for stars with and without seismic detections. Left: correspondence between the global asteroseismic νmax parameters and the high-frequency PSD noise level for stars with positive seismic detections in blue, for stars identified as classical pulsators (e.g. δ Sct/γ dor), in red, and for stars without a seismic detection, in green. The νmax and colouring of the non-detection cases are given by the νmax and detection probability (Pdet) returned by the ATL3 (Hey et al. 2024), and except for a few cases, we only include stars with Pdet > 50%. The non-detection νmax values have been offset (horizontal line indicates the offset from the original position) by increasing νmax by 16%, corresponding to the apparent overall bias of the ATL3 values, as seen from Fig. 6. Stars with a noise level above 20 ppm2/μHz have been offset to this value (dotted line). The coloured lines give the median binned noise levels of stars with (blue) and without (green) detections against νmax. Right: correspondence between the maximum ATL3 Pdet (for either 20- or 120-s cadence) against νmax, with the same colouring and νmax as in the left panel. Stars with a Pdet below 50% have been offset to this value (dotted line). For the stars with seismic detection (blue), we indicate with small horizontal lines the ATL3 predicted νmax.
thumbnail Fig. 9

Aitoff sky projection in Galactic coordinates showing (circular markers) the TLS, with the marker size corresponding to the star’s visual magnitude (see legend key). The PLATO long-stare (LOP) fields are shown in blue (with shade corresponding to the number of 24, 18, 12, or 6 overlapping cameras). Yellow markers indicate targets identified as being within current PLATO LOP field definitions (see text), while orange markers indicate targets within 5° of the LOP field boundaries. Stars that are in binary systems and listed in Table D.5 are further marked with a cross (×). The TESS CVZs are given by the red circles that partly overlap the PLATO fields. The red line indicates the sky’s equatorial plane, while the green line gives the ecliptic plane.

5.4 TLS and the Habitable Worlds Observatory

The Habitable Worlds Observatory (HWO) is a planned NASA 6-meter-class UV/optical/IR space observatory capable of high-contrast imaging and spectroscopic characterisation of potentially habitable exoplanets in reflected light. The HWO concept was the top priority of the 2020 Decadal Survey on Astronomy and Astrophysics and is currently envisioned to launch in the early 2040s.

The primary targets for HWO are nearby, bright Sun-like stars for which the inner working angle allows for the detection of planets on angular separations consistent with the habitable zone. Possible targets for HWO are described in a catalogue of HWO Precursor Science Stars by the NASA Exoplanet Exploration Program (ExEP) (Mamajek & Stapelfeldt 2024) and in the HWO Input Catalog (Tuchow et al. 2024). The former consists of 164 stars divided into tiers (A, B, or C), based on their expected suitability for detecting Earth-like exoplanets.

Of the 164 ExEP HWO targets, we have identified 139 matching the selection criteria adopted in this analysis (Sect. 2). The remaining stars include α Cen A+B, which were not included in our analysis, as well as stars fainter than our V = 6 selection cut that mostly have spectral types later than K0V (hence, with low probability of detecting oscillations). Of the 139 stars matching our selection criteria, two stars (τ6 Eri and 12 Oph) have not been, and are not scheduled to be, observed by TESS, while eight stars8 did not have any data up until Sector 77, as considered in this analysis. From the remaining 129 stars with data, we detected oscillations in 67 stars, including 20 from tier A, 22 from tier B, and 25 from tier C. The HWO sample is shown in Fig. 10 in terms of luminosity against distance (following the illustration of the sample in Mamajek & Stapelfeldt 2024), with indications of which stars have detections. In Table D.1, we have for all stars with seismic detections indicated their tier if they overlap with the HWO sample.

The detections presented here, combined with asteroseismology of cooler HWO targets with extreme precision radial velocities (e.g., Campante et al. 2024; Hon et al. 2024; Li et al. 2025), will allow for the systematic determination of precise ages of HWO targets, which are critical for interpreting possible biosignatures from directly imaged planets (Bixel & Apai 2020). A future paper in this series will focus on the asteroseismic age distributions for the HWO sample (Chontos et al., in prep.). Continued 20-s data of bright nearby stars in future TESS extended missions will be important to expand upon the sample of detections presented here.

5.5 Exoplanets, discs, and substellar objects

Detailed stellar characterisation from asteroseismology of known exoplanet hosts is of great interest, including mass estimates of RV exoplanets, information on system ages, and obliquities. (Van Eylen et al. 2014; Huber 2018; Lundkvist et al. 2018; Lund et al. 2019).

Among the oscillating stars in the TLS (Table D.1), we have identified 24 as known exoplanet hosts, of which 13 are also in the HWO target list (Sect. 5.4). In 12 of these 24 systems, ours are the first asteroseismic measurements, including one brown dwarf host (HD 46588/HIP 32439), one star that is positioned within the current definition of the PLATO LOPN1 field (HD 184960/HIP 96258; Sect. 5.3), and several notable multiplanet systems, such as v And (HIP 7513/Titawin), 47 UMa (HIP 53721/Chalawan), 61 Vir (HIP 64924), and 82 Eri (e Eri/HIP 15510), which Nari et al. (2025) recently found to host a super-Earth orbiting in the star’s habitable zone.

In Table D.3, we provide an overview of the known exoplanet systems in the TLS, whose derived planet properties can be tightened thanks to the precise stellar radii, masses and ages delivered by an asteroseismic analysis. In addition to exoplanets, the table also lists any sub-stellar companions (such as brown dwarfs), and information on binarity (see also Table D.5). The planetary data provided in Table D.3 was primarily obtained from the “Planetary Systems” of the NASA Exoplanet Archive9, and supplemented by information from the Extrasolar Planets Encyclopedia10.

Also worth mentioning is ψ1 Dra B (HIP 86620), which is known to host a long-period giant exoplanet (ψ1 Dra Bb) with a minimum mass of 1.5 MJup and an orbital semi-major axis of 4.4 AU (Endl et al. 2016). Though we did not identify oscillations in ψ1 Dra B, we did in its companion ψ1 Dra A (see Table D.5 and Fig. 2) and asteroseismology can therefore anchor the age for the whole system. We note also that ψ1 Dra A/B are within the current definition of the PLATO LOPN1 field.

Finally, several of our identified asteroseismic stars are known to host debris discs (Hughes et al. 2018; Pearce 2024). Notable examples include binary systems (Table D.5) such as 99 Her (b Her/HIP 88745), which hosts a nearly polar-aligned circumbinary debris disc (Kennedy et al. 2012; Smallwood et al. 2020), and HD 121384 (HIP 68101) (Rhee et al. 2007; Rodriguez & Zuckerman 2012); HD 132254 (HIP 73100) and 110 Her (HIP 92043) have been identified as hosts of cold debris discs (Krivov et al. 2013; Marshall et al. 2013); and known exoplanet hosts, such as 82 Eri (e Eri/HIP 15510) (Pepe et al. 2011; Montesinos et al. 2016) and 61 Vir (HIP 64924) (Wyatt et al. 2012) also exhibit debris discs.

Access to well-characterised stellar parameters from asteroseismology, particularly stellar ages, is crucial for understanding the evolution of debris discs. These parameters provide a critical context for interpreting the current state of the discs, including their composition, structure, and dynamical processes, while also constraining the history of planet formation and interactions within these systems (Trilling et al. 2008; Montesinos et al. 2016). Moreover, accurate stellar characterisation enables meaningful comparisons across different systems, thereby enhancing our ability to discern patterns and trends in the evolution of planetary systems.

thumbnail Fig. 10

Habitable Worlds Observatory target stars in terms of their luminosity and distance, with values adopted from the tables of Mamajek & Stapelfeldt (2024). Filled markers indicate stars with detected oscillations, with the colour and shape indicating the HWO tier (see legend). Stars with unfilled black thick-edged markers were considered for analysis, while black thin-edged ones did not conform to the selection criteria for our sample. Stars with red thick-edged markers were considered but did not have data available before S77, as considered in this analysis (but will later in Cycle 7), while red thin-edged ones are not scheduled for observations with TESS. The legend showing the different tiers gives, in parentheses, the number of detections out of the total number of stars in the tier, and lastly, the number of stars from each tier that we analysed.

5.6 Interferometry for angular stellar diameters

Long-baseline interferometric observations for the measurement of stellar angular diameters provide an essential ingredient for obtaining stellar fundamental parameters of the highest precision that are nearly model-independent (apart from a small dependence on the adopted limb-darkening). Most importantly, an independent estimate is provided for the stellar linear radius by incorporating the distance (e.g. from Gaia). Similarly, combining an estimate of the stellar bolometric flux with the measured angular diameter provides an independent Teff that, when combined with the independent radius and νmax, provides an estimate of the stellar mass (Pijpers et al. 2003; Cunha et al. 2007; Creevey et al. 2007; Bruntt et al. 2010; Bazot et al. 2011; Huber et al. 2012; White et al. 2013, 2018).

Identifying stars with both interferometric and asteroseismic measurements is therefore essential for calibrating asteroseismology, both in terms of the application of scaling relations and the fine details of model physics. In our search for existing interferometric stellar diameter measurements, we cross-referenced our sample against the compilation of Baines et al. (2023) (their Table 9), the Jean-Marie Mariotti Center (JMMC; Bourgés et al. 2014) Measured Stellar Diameters Catalog (JMDC, Cat II/345/jmdc; Duvert 2016, introduced as part of Chelli et al. (2016)) (updated last 13 Sep. 2021), the list of CHARA published interferometric diameters11, and the samples of Rains et al. (2020), Karovicova et al. (2022), and North et al. (2007).

In Table D.4 we list the 54 stars for which a published measurement could be found from the adopted compilations. We identify 31 of these as being new oscillators, significantly expanding the cohort of MS/SG stars suitable for testing asteroseismology with interferometry. In Fig. 11 we provide an overview of the TLS in terms of visual magnitude and declination, and indicate the stars with existing interferometric measurements for stellar diameter determination. Brighter than V = 4.5, we identify only 6 targets (82 Eri, ζ Tuc, α Cha, γ Pav, ψ Cap, and HD 60532) where we could not identify an interferometric measurement in the literature. For each star, Fig. 11 also provides a simple estimate of the expected angular diameter from combining distance (from the TIC) with a seismic radius obtained from scaling relations using our measured νmax and Δν in combination with a Teff from Casagrande et al. (2011) (or the TIC if not available).

We note that all stars have a predicted angular diameter above ~0.38 mas. Combined with the brightness of the sample, all stars in the TLS (except some binaries) should be accessible for interferometric observations with either CHARA (Centre for High Angular Resolution Array; ten Brummelaar et al. 2005) or NPOI (Navy Precision Optical Interferometer; Armstrong et al. 1998) for the more northern targets, and VLTI (Very Large Telescope Interferometer; Glindemann et al. 2001) for the more southern targets.

thumbnail Fig. 11

TESS luminaries stars plotted in terms of visual magnitude against declination, with the colour indicating the expected angular diameter (see colour bar). Stars with a thick outline have a published interferometric angular diameter (Table D.4), and stars with a cross (×) are binaries listed in Table D.5.

5.7 Binarity

Stars exhibiting solar-like oscillations whilst also being members of binary systems are particularly important if they can be characterised spectroscopically (preferably with solutions for both components), in addition to either being eclipsing or having their orbit traced on the sky (either from resolved or interferometric observations for visual binaries or from astrometric observations). The constraint offered by the binarity in providing nearly model-independent estimates for the stellar mass (individual masses in the best cases and minimum masses in the worst) is of paramount importance for testing the masses provided by asteroseismology (Serenelli et al. 2021).

To date, when focusing on constraining the asteroseismology of solar-like oscillators, the effort has mainly been on eclipsing spectroscopic binary systems typically containing one or two evolved red giants (e.g., Gaulme et al. 2016; Brogaard et al. 2018, 2022; Benbakoura et al. 2021; Thomsen et al. 2022). In cases where oscillations are detectable in both components of a binary system containing MS and/or SG stars (Miglio et al. 2014), the binary period is often too long or the orbital configuration cannot be constrained to a degree that allows for independent constraints to be placed on the masses (Metcalfe et al. 2015; White et al. 2017; Li et al. 2018; Joyce & Chaboyer 2018), whereby only the asteroseismic ages can be tested against the assumed coevality of the stars. This lack of added constraint on the stellar mass is also, in general, the case for the known binary systems with one well-characterised MS/SG oscillating component (Kjeldsen et al. 1995; Deal et al. 2017; Grundahl et al. 2017; Metcalfe et al. 2021; Ball et al. 2022). Given these challenges, only very few asteroseismic analyses of MS/SG stars have currently benefited from the added constraints of binarity in the stellar modelling (Appourchaux et al. 2015; Metcalfe et al. 2020), or have been able to serve as benchmarks of asteroseismology.

Given the brightness of the stars in our sample, many have been subjected to extensive studies and are generally well-characterised. To identify known binary stars in our sample that could become important benchmarks of asteroseismology, we cross-referenced against the Washington Double Star catalogue (WDS; Mason et al. 2014), the Ninth Catalogue of Spectroscopic Binary Orbits (SB9; Pourbaix et al. 2004), the Sixth Catalog of Orbits of Visual Binary Stars (ORB6; Hartkopf et al. 2001a), and the Observatorio Astronómico Ramón María Aller Catalog of Orbits and Ephemerides of Visual Double Stars (OARMAC; Docobo et al. 2001, 2012). We note that many stars in the sample have a WDS designation. However, we are interested in systems where constraints can be placed on the orbits rather than simply having information on the existence of companions, and we require as a minimum, an estimate of the orbital period. In addition to consulting the above catalogues, we conducted an extensive literature search of all the asteroseismic targets. The result of our search is given in Table D.5. As an additional check, we matched our sample against the Gaia DR3 non-single star (NNS) catalogue gaiadr3.nss_two_body_orbit (Halbwachs et al. 2023; Holl et al. 2023). We found seven stars12 in the NNS catalogue, but with no new additions beyond the ones already identified from the other catalogues. Of the seven stars, all but two (HIPs 86036 and 5081) have orbital periods from the Gaia NNS catalogue in agreement with the values listed in Table D.5, and both poorly matching stars have goodness_of_fit and/or significance values indicating a poor NNS solution. Only in the case of 35 Leo (HIP 50319) can the Gaia solution bring to bear information on the orbit (in the form of Campbell elements13) not available from existing observations.

In addition to catalogue identifiers, period measurements and semi-amplitudes (for spectroscopic systems), we also indicate which of the stars are located within or near the PLATO LOP fields (Sect. 5.3). We also provide literature estimates of v sin i, since they can influence the quality of ground-based spectroscopic follow-up efforts. Unless otherwise stated in Table D.5, notes via a letter reference to the spectroscopic periods (“P (Spec)”), and semi-amplitudes (“K1(/K2)”) were obtained from SB9, adopting the latest entry if multiple exist (a numerical reference to this entry is provided in the table). The visual/astrometric period (“P (Orb)”) was generally obtained from ORB6, if not otherwise indicated with a letter reference, and uncertainties in parentheses provide any non-zero root-mean-square-deviation between multiple entries in ORB6 and/or OARMAC. The table is meant to give an overview of the feasibility of follow-up observations of the stars and their potential use as benchmarks. Therefore, we have generally omitted uncertainty estimates on the parameters (see table notes).

Of the 48 asteroseismic stars with available binary orbital information listed in Table D.5, a significant fraction could become valuable benchmark systems for asteroseismology, and several are likely to be observed by PLATO (Sect. 5.3). Seven stars are listed as SB2 systems with additional orbital constraints from being visual/astrometric binaries; for these individual components, masses can be directly determined and compared to the asteroseismic values. With additional follow-up observations, more systems could potentially be identified as SB2, and later Gaia releases should provide orbital constraints for more of the spectroscopically characterised systems.

One facility that is well-suited to providing follow-up spectroscopic observations of the binary orbits for bright stars, and in many cases also for asteroseismology, is the Stellar Observations Network Group (SONG; Grundahl et al. 2017). In Table D.5, we provide an overview of the current observations conducted for these stars using SONG14 and note that most of the stars with periods below ~7 yr have already been scheduled for long-term monitoring. Some of the stars with high numbers of existing spectra from SONG will be the subject of future dedicated analysis of both the binary and asteroseismic data. This includes such stars as ω Dra (HIP 86201), ι Peg (HIP 109176), and χ Dra (Rudrasingam et al., in prep.). In Appendix E, we provide notes concerning binarity for several individual stars, in some cases to elaborate on the information in Table D.5 and in some to clarify why stars identified as binaries, such as on SIMBAD, have been excluded.

5.8 Solar analogues with seismic detections

Identifying and characterising stars that resemble the Sun is important because they provide an essential context for understanding the Sun in terms of evolution, activity, and chemistry, and they are naturally of interest in the search for exoplanets. Sun-like stars come in different categories, depending on their resemblance to the Sun, where “solar twins” are restricted to having near-solar parameters on all fronts, while “solar analogues” are more loosely defined as having parameters “similar” to the Sun (Hardorp 1982; Cayrel de Strobel 1996). The definition of a solar analogue is not very stringent, and the criteria used in the literature for their identification vary. However, to give an overview of the subsample of (potential) interest for solar-analogue studies, we applied the criteria of Teff within ±500 K of the Sun, [Fe/H] within ±0.3 dex (corresponding to a metallicity within a factor of two of solar), and MV within ±1 magnitude of the solar at MV,⊙ = 4.83 (Soderblom & King 1998)15. We have not restricted the subsample to be without close companions, but refer to Sect. 5.7 (Table D.5) for information on binarity.

To have a homogeneous source for the stellar Teff and [Fe/H], we adopted values from the Geneva-Copenhagen Survey (Nordström et al. 2004) in the revised version by Casagrande et al. (2011). Distances and magnitudes were adopted from the TESS Input Catalog (TICv8.2; Paegert et al. 2021). In Fig. 12 we show the subsample that meets the above criteria (though we have still included stars with [Fe/H] beyond the limits). As seen, our sample contains dozens of potential solar analogues that can now be characterised asteroseismically, many of which are well-known from several spectroscopic compilations of solar analogues (e.g., Cayrel de Strobel 1996; Ramírez et al. 2009; Porto de Mello et al. 2014; Datson et al. 2015).

We can tighten the required resemblance to the Sun, in line with the definition sometimes used for a solar-twin (e.g., Adibekyan et al. 2017), to Teff = 5772 ± 100 K and log g = 4.44 ± 0.1 dex (calculated from Teff and νmax, see Lund et al. 2024). This identifies four stars, namely: ν2 Lup (HIP 75181; a known multiplanet system, see Udry et al. 2019; Kane et al. 2020), 51 Peg (HIP 113357; a known exoplanet host, Mayor & Queloz 1995), 26 Dra (HIP 86036; a known long-period visual and spectroscopic binary, see Table D.5), and HD 102365 (HIP 57443; a known exoplanet host, see Tinney et al. 2011). Two of these have already been studied with asteroseismology: 51 Peg (Metcalfe et al. 2024) and ν2 Lup (Delrez et al. 2021; Weeks et al. 2025). As suggested by the Casagrande et al. (2011), [Fe/H] values for these stars (Fig. 12) are all outside the limit of [Fe/H] ± 0.1 dex corresponding to the tightened limits on Teff and log g (but all within [Fe/H] ± 0.3 dex). In general, we find a broad consensus between the [Fe/H]-values from Casagrande et al. (2011) with those found from the literature, except for 26 Dra which in many other studies are found to have a near-solar metallicity (see, e.g., Ramírez et al. 2013; Tautvaišienė et al. 2020; Fuhrmann 2008; Soubiran et al. 2022). Of interest for future studies, 26 Dra is also inside the current northern PLATO long-stare field (see Sect. 5.3).

thumbnail Fig. 12

Stars identified to match our solar-analogue criteria (see Sect. 5.8). The marker size indicates the visual magnitude (see legend), while the colour gives the [Fe/H]-value from Casagrande et al. (2011). Empty markers indicate known bright oscillators from ground-based surveys (and 16 Cyg B from Kepler) that match the solar-analogue criteria, but where no detection was obtained in this study. Markers with a thick black edge indicate the stars that are already known oscillators (see Table D.1). Left: identified (potential) solar analogues in terms of distance and νmax (α Cen A at a distance of ~1.35 pc is moved to a higher distance for a better display of the sample). The horizontal dotted line gives the solar νmax-value for reference. Right: identified (potential) solar analogues in terms of Teff and luminosity, using magnitudes and distances from the TESS Input Catalog (TICv8.2; Paegert et al. 2021). The dotted lines provide the solar values for reference. For reference, we also show 1 M⊙ MIST16 evolutionary tracks (Dotter 2016; Choi et al. 2016) with [Fe/H]-values of −0.3, 0, and +0.3 dex (increasing with tracks from left to right; also see color bar).

5.9 Notes on individual targets

In the following, we list a few targets of potentially high interest that have not been discussed in detail in the previous sections. These targets are not necessarily mentioned because of the quality of their seismology, but for the potentially improved understanding we may gain from these stars/systems through the information offered by an asteroseismic analysis.

θ UMa (HIP 46853): A very bright (V = 3.17) SG solar-like oscillator (see Fig. 3), comparable in brightness to η Boo, β Hyi, ζ Her, and μ Her. While predicted by Bedding et al. (1996) to show oscillations, it has until now escaped a detailed observational investment for an asteroseismic characterisation. θ UMa has independent interferometric measurements of its diameter (Table D.4), and is currently being observed in RV with SONG for asteroseismic analysis.

χ Dra (HIP 89937): A bright (V = 3.55) newly detected solar-like oscillator (F7V; see Fig. 3) that is a member of a double-lined spectroscopic (SB2) and visual binary system (see Table D.5), with a K1V companion. The binary orbit (P = 280.5 d) is extremely well-determined from RV observations, including many observations from SONG, from which individual component spectra can be disentangled for abundance analysis. In addition to the SB2 characterisation the visual orbit is well constrained from astrometric observations (e.g., Hartkopf et al. 2001b), allowing individual masses to be measured, and interferometric observations have been obtained using CHARA for independent constraint on the stellar radius. Importantly, χ Dra is included in PLATO’s current northern LOP field and promises to become a key benchmark star for asteroseismology. A detailed system analysis will be presented by Rudrasingam et al. (in prep.).

HR 3220 (HIP 39903/B Car): A known single-lined spectroscopic binary (Murdoch & Hearnshaw 1991, 1993) and visual binary (Goldin & Makarov 2006, 2007) with an orbital period of P ~ 900 d (see Table D.5 and Fig. 3). Fuhrmann et al. (2011a) identified HR 3220 as a field blue straggler with a white dwarf companion. This was based on a match between the estimated secondary mass and the Rappaport et al. (1995) white dwarf mass-period relationship, and the measured [Fe/Mg] = −0.27 abundance (and metallicity of [Fe/H] = −0.27), which suggest an old (τ ~ 8–10 Gyr) star (e.g., Nissen et al. 2020). We note that Brown et al. (2000) indicated the potential existence of a substellar companion, based on IR-excess observed with the Hubble Near-Infrared Camera and Multi-Object Spectrometer (NICMOS) Camera 2 coronagraph (see also Schultz et al. (2014), who suggested that the companion is a rare brown T-type dwarf, which is very uncommon to find around an F-star like HR 3220). Finally, we note that HR 3220 is within the PLATO LOPS2 field (Table D.2).

ι Peg (HIP 109176) and ω Dra (HIP 86201): Both of these stars are members of well-characterised visual and SB2 systems (Table D.5) with short orbital periods (e.g., Boden et al. 1999; Morel et al. 2000; Fekel et al. 2009; Konacki et al. 2010; Behr et al. 2011). Similar to χ Dra, it should therefore be readily possible to provide dynamical mass estimates to test the results from asteroseismology (with the caution that the assumption of isolated stellar evolution in an asteroseismic modelling effort could be questionable). ω Dra is furthermore within the current PLATO LOPN1 field (Table D.2).

171 Pup (HIP 37853): This star is the primary of a wide binary system containing the common proper motion companion star VB3 (van Biesbroeck 1961), identified by Kunkel et al. (1984) to be a low-luminosity white dwarf of spectral type DC9-11 (Wesemael et al. 1993). VB3 is often identified as WD 0743-336 (and sometimes WD 0743-340, GJ 288B or NLTT 18414B) and is one of the coolest WD stars and widest binaries known amongst Sirius-like-systems, which comprise a WD and a star of spectral type K or earlier (Holberg et al. 2013) (see also Bergeron et al. 2001; Holberg et al. 2008; McCook & Sion 2016). Holberg et al. (2013) provides a period for the system of 1.38 Myr, a separation of 14682.4 AU, and masses of 1.08 and 0.59 M⊙ for the components. The MS A-component is found to consist of a close pair (WDS discoverer designation TOK 193 Aa,Ab; resolved in speckle interferometry by Hartkopf et al. (2012) and Tokovinin (2012)) for which Tokovinin (2014) gives a period of 8.258 yr (the ORB6 database provides a period of 23.10 yr based on later observations by Tokovinin). We find that this star/system would be very interesting to study in relation to comparing the asteroseismic age of the MS star 171 Pup A with the estimated cooling age of VB3, which could help to empirically constrain the age determination for the oldest WDs. From the age difference, the pre-WD lifetime could be estimated, which could provide the WD progenitor mass and thereby help to constrain the initial-final mass relation. To date, only very few binary systems containing a WD and an oscillating companion have been identified, and these often tight systems contain an early-type MS primary star that does not readily allow an age to be determined using asteroseismology. Examples are the EL CVn binaries consisting of an A- or F-type primary and a low-mass helium white dwarf (WD) secondary, where the primary occasionally is found to oscillate as a δ Scuti pulsator (e.g., Maxted et al. 2014; Guo et al. 2017).

o2 Eri (HIP 19849/40 Eridani A17): This star is part of a triple-star system, orbited (P~8000 yr, Table D.5) by a binary (B and C components) consisting of a WD (o2 Eri B/WD 0413-077; type DA2.9 and the first ever recognised WD) and an M-dwarf (o2 Eri C) in a 230-year-long orbit (Bond et al. 2017; Mason et al. 2017). As for 171 Pup, an asteroseismic age determination could be compared with the WD age, as obtained from cooling tracks and initial–final mass relations.

31 Aql (HIP 95447/b Aql): With a metallicity of [Fe/H]~0.35 dex, this star was analysed by Mishenina (1996) and Feltzing & Gonzalez (2001) (among others) in the context of being a “super-metal-rich” (SMR) star.

HD 76932 (HIP 44075): This star was listed by Nissen & Schuster (2011) as a thick-disc star, and Fuhrmann et al. (2017) found it to be discrepant in [Ba/Fe] versus [Fe/H], suggesting it to have a WD companion.

HD 65907 (HIP 38908): Fuhrmann et al. (2012) analyzed this star in the context of being an old Pop II star based on its abundance but found this to conflict with the age derived from evolutionary tracks, which they explained as being caused by a former mass transfer. An asteroseismic age could potentially help resolve the issue.

HD 81809 (HIP 46404): This star is well-studied in the context of stellar activity, and long-term X-ray monitoring has shown a well-defined chromospheric activity cycle with a period of 8.05 ± 0.07 yr (Bonanno & Corsaro 2022, see also Orlando et al. 2017; Egeland 2018), and found to have a suggested dynamo action similar to that of the Sun. Moreover, the star is a member of a well-defined visual and spectroscopic (SB2) binary system (see Table D.5). The star was studied asteroseismically (Table D.1) by Corsaro et al. (2024) in the context of oscillation amplitude suppression from magnetic activity.

HD 156098 (HIP 84551): Analysis by Feng et al. (2022) listed this star as the host of two potential exoplanets with periods of 21.85 ± 0.01 and ***(eq1)***7841−517+247$\[7841_{-517}^{+247}\]$ days. The star is also commonly used as a comparison star in the analysis of the GRO J1655-40 system (Foellmi 2009), where the companion was found to be a black hole (Orosz & Bailyn 1997; Mirabel et al. 2002).

HD 186155 (HIP 96825; HR 7495; KIC 9163520): This star was included in our initial selection and was identified as a so-called “Hump-and-Spike” (H+S) star by Pope et al. (2019) based on long-cadence (30-min) smear data from Kepler. It has been analysed recently in this context by Henriksen et al. (2023) and Antoci et al. (2025). Based on the star’s H+S classification and its location in the HR diagram (positioned in a region where solar-like oscillations are not expected), this star is not included in the final sample. However, we note that clear excess power akin to solar-like oscillations was identified at ν ~ 250 μHz, in addition to the low-frequency peaks associated with the H+S characteristics. The origin of this excess power remains to be understood.

6 Conclusions

With the TLS, we have provided detections of asteroseismic signals for a total of 196 MS/SG stars, visible to the naked eye (V ≤ 6). Of these, to the best of our knowledge, 128 are new detections. Given the brightness of this sample compared to most asteroseismic stars from Kepler, it is possible to obtain a high-quality characterisation of the stars from ground-based efforts, and many of the stars already have extensive literature from decades of scrutiny. Our goal with this analysis has been to report on the asteroseismic detections, to encourage continued observations from TESS, and to highlight the many potential uses of the sample in future in-depth analyses.

In processing the TESS data, we used the products produced by SPOC (Jenkins et al. 2016) and the light curves extracted from the TPF using custom apertures (Sect. 3 and Appendix B). While we found neither data product to be superior in general, we consider them highly complementary; the custom apertures allowed us in several cases (see, e.g., Fig. 2) to make detections of oscillations (or improve upon these) where the SPOC apertures were ill-defined. From our processed light curves, we confirmed the apparent superior quality of 20-s over 120-s cadence data identified by Huber et al. (2022) and found this to extend to the brighter stars in the TLS (Sect. 5.2).

We have provided values for the global asteroseismic parameters Δν and νmax from the PySYD pipeline for all stars with identified oscillations (Sect. 5.1, Table D.1), and all reported detections have been confirmed by three independent pipelines. We found excellent overall agreement with values from the literature for the stars with previous detections (Sect. 5.1) and, with only two exceptions, we were able to confirm all previous detections based on TESS data (Sect. 5.2). In our comparison of measured νmax values with those expected from the ATL3, we have identified an apparent underestimation of νmax from the latter, which leads to overly optimistic detection probabilities (Sect. 5.1 and Fig. 6). The bias in νmax estimates from ATL3 is probably caused by biases in the Gaia DR3 estimates of Teff and log g (which is especially clear for the brightness range covered by our sample) used in the νmax calculation (Appendix C).

The TLS also contains several groups that are of potential interest, as follows. We identified a total of 34 stars overlapping the current LOPN1 or LOPS2 field definitions of PLATO, and thereby of interest for the calibration or validation of the asteroseismic parameters returned from this upcoming mission (Sect. 5.3). Several TLS stars are of potential interest to studies of solar-analogues (Sect. 5.8). We identified 54 stars that have long-baseline interferometric observations (Sect. 5.6, Table D.4), providing independent measurements of their radii. We identified 48 stars that are members of stellar binaries where an orbital characterisation has been possible. Of these, 23 (with 9 being SB2 systems) have both spectroscopic and visual/astrometric constraints providing independent constraints on the stellar masses (Sect. 5.7, Table D.5). We identified 24 exoplanet-host stars (Table D.3), 12 of which are without previous detections of oscillation (Sect. 5.5), and all except 11 systems are also included in HWO target list. Finally, we identified several individual stars/systems where the detection of oscillations could be of particular interest for dedicated in-depth studies (Sect. 5.9), including the bright (V = 3.17) SG θ UMa and the well-characterised SB2 system χ Dra (Rudrasingam et al., in prep.).

In addition to our measurements of the global asteroseismic parameters, we estimate that ~63% of the stars are amenable to peak-bagging for the analysis of individual mode parameters – this analysis and stellar modelling of the sample will be the subject of a future analysis. In future work, we will also extend our analysis to the correspondingly bright evolved stars observed by TESS (Fig. 4), and we will provide updates and extensions to the TLS following the continued collection of data from TESS.

Data availability

Tables A.1, D.1, D.2, D.3, D.4, and D.5 are available at the CDS via https://cdsarc.cds.unistra.fr/viz-bin/cat/J/A+A/701/A285.

Acknowledgements

We thank the anonymous referee for useful comments that helped improve the initial version of the paper. The authors acknowledge the dedicated team behind the TESS mission, without whom this work would not have been possible. We recognise the PIs of the TESS Guest Investigator proposals that, over the years, have ensured the 120- and 20-s cadence observations of the stars analysed here (see https://heasarc.gsfc.nasa.gov/docs/tess/approved-programs.html). We are grateful to Daniel Hey for discussions on the ATL, to Pierre Maxted, Hugh Osborn, Valerio Nascimbeni, and Juan Cabrera for discussions on the PLATO LOP fields, and to Vichi L. Antoci for discussions on individual classical pulsators in the sample. M.N.L. acknowledges support from the ESA PRODEX programme (PEA 4000142995). S.M. acknowledges support from the Spanish Ministry of Science and Innovation with the grants number PID2019-107061GB-C66 and PID2023-149439NB-C41, and through AEI under the Severo Ochoa Centres of Excellence Programme 2020–2023 (CEX2019-000920-S). R.A.G. acknowledges the support from the GOLF and PLATO Centre National D’Études Spatiales grants. T.R.B. acknowledges support from the Australian Research Council (FL220100117). D.H. acknowledges support from the Alfred P. Sloan Foundation, the National Aeronautics and Space Administration (80NSSC22K0303, 80NSSC23K0434, 80NSSC23K0435, 80NSSC21K0652) and the Australian Research Council (FT200100871). This research has made use of the Washington Double Star Catalog maintained at the U.S. Naval Observatory. This research has made use of the SIMBAD database (Wenger et al. 2000), operated at CDS, Strasbourg, France. This research has made use of the Jean-Marie Mariotti Center (JMMC) Measured Stellar Diameters Catalogue (available at http://www.jmmc.fr/jsdc) and the OiDB service (available at http://oidb.jmmc.fr). This research has made use of the NASA Exoplanet Archive, which is operated by the California Institute of Technology, under contract with the National Aeronautics and Space Administration under the Exoplanet Exploration Program. This research has made use of data obtained from or tools provided by the portal exoplanet.eu of The Extrasolar Planets Encyclopaedia. This work presents results from the European Space Agency (ESA) space mission Gaia. Gaia data are being processed by the Gaia Data Processing and Analysis Consortium (DPAC). Funding for the DPAC is provided by national institutions, in particular the institutions participating in the Gaia MultiLateral Agreement (MLA). The Gaia mission website is https://www.cosmos.esa.int/gaia. The Gaia archive website is https://archives.esac.esa.int/gaia. This research made use of the SONG database SODA (https://soda.phys.au.dk/), operated and maintained at Aarhus University, DK. We acknowledge the use of the following Python-based software modules: Astropy (Astropy Collaboration 2013), PyAstronomy (Czesla et al. 2019), Lightkurve (Vinícius et al. 2018), KDEpy (Odland 2018), scikit-image (van der Walt et al. 2014), platopoint, tess-atl (Hey et al. 2024), tpfi (Xing et al. 2024), and pySYD (Chontos et al. 2022).

Appendix A Stars without observations from TESS

Table A.1 provides an overview of the stars that met our selection criteria in terms of MV and B − V, but did not have TESS observations up to and including Sector 77 as considered in this analysis. Table A.1 lists both the 69 stars that will be observed during Cycle 7 and the 19 stars that are not scheduled for observations.

Table A.1

Stars fulfilling TLS selection criteria, but with no data before Sector 78.

Appendix B Comparing SPOC and custom apertures

As mentioned in Sect. 3, we use, in addition to data from SPOC light curves extracted from the TESS TPFs using custom apertures made from the combination of SPOC and K2P2 apertures (Lund et al. 2015). In Fig. B.1 we compare the joint apertures and those from SPOC based on 120-s cadence data, and as seen the aperture sizes follow the expected trend against magnitude18. Per definition, the joint apertures will always be equal to or larger than the one from SPOC alone, but we can see that from a TESS magnitude (Tmag) of ~4.7 and below the aperture from SPOC contributes an increasing fraction of pixels to the joint aperture – this is the magnitude region where the blooming trails from saturation become significant. Above Tmag~4.7, the aperture from K2P2 will typically fully contain the SPOC aperture. As expected, we see (not shown) little (±1 − 2 pixels) to no difference between the apertures defined for 120-s versus 20-s cadence data.

The aperture sizes in the left and middle panels of Fig. B.1 are median values if stars are observed in multiple sectors. For a given star, there is some scatter in the aperture sizes defined for different sectors. There can be many causes for such scatter, such as, from the variation in TESS PSFs across the focal plane which could add to the scatter for a given star from varying boresight distances (Vanderspek et al. 2018), and this effect would also add to the width of the aperture size distribution of the given Tmag. From the right panel of Fig. B.1 we see that at least part of the scatter in aperture sizes for a given star is related to the observing sector. The plot shows for a given star the difference in aperture sizes between SPOC and K2P2 for a given sector (i) compared to the median of the SPOC and K2P2 apertures across all observing sectors for the star. We see that on this scale (and with the adopted settings for the threshold for selecting pixels of interest), the K2P2 apertures are in median ~40% larger than the SPOC ones across all magnitudes and sectors. There are, however, clear variations between different sectors, or rather between different pointings/cycles – in Cycle 1 (southern ecliptic hemisphere; sectors 1-13) the K2P2 apertures are larger than SPOC as compared to Cycle 2 (northern ecliptic hemisphere; sectors 14-26), and the largest effect is seen for the fainter stars in the sample. One might speculate that this relates to different observing conditions in the different sectors/pointings, and indeed variations in scattered light or pointing stability could and might have an effect, but an important aspect also seems to be the TPF stamp sizes (Fig. B.2).

In Fig. B.2 we show the variation in TPF stamp widths and heights as a function of magnitude and observing sector. As expected, a strong dependence on the stamp height (direction of potential blooming trails) on magnitude is seen, while the width has a value of 11 or 25 pixels. In the early cycles (1-3, sectors 1-39) there is a general tendency for brighter targets observed in 120-s cadence to have widths of 25 pixels while fainter targets adopt widths of 11 pixels. For 20-s cadence observations (introduced from Cycle 3, starting with Sector 27), this tendency is mainly observed for Cycle 3 (sectors 27-39) – later sectors almost exclusively adopt widths of 11 pixels. Concerning the stamp heights, we see that a value of 25 pixels is representative in median across cycles and cadences.

thumbnail Fig. B.1

Comparison between SPOC and K2P2 apertures. Left: Median (across sectors) aperture size in pixels against TESS magnitude for stars observed in 120-s cadence for apertures defined by SPOC and the adopted joint custom K2P2 + SPOC apertures (see legend). Middle: Number of pixels contributed to the custom apertures by SPOC (i.e., number of pixels not contained in the K2P2 aperture) as a function of TESS magnitude. Right: Difference in K2P2 and SPOC aperture size relative to the median, with points colour-coded according to TESS magnitude. The dashed horizontal line provides the median level across sectors, while vertical lines indicate shifts in pointing (see legend), typically coinciding with different sectors.
thumbnail Fig. B.2

Properties of TESS pixel stamps for 120-s (top row) and 20-s (bottom row) cadence observations. First column: Median pixel stamp width and height across sectors for individual stars as a function of their TESS magnitude. Second (third) column: Stamp width (height) as a function of sector, coloured by TESS magnitude. Width values have been dithered by up to ±0.5 pixels to show any dependence on magnitude better. Vertical lines indicate changes in pointings (see Fig. B.1, right panel). For the heights, red markers indicate stars that are found to have a pixel stamp height that is smaller than two or more fainter stars, i.e., breaking with the general monotonic increase in stamp height with decreasing TESS magnitude seen on the first column. Fourth column: Total stamp size as a function of sector, colour-coded according to TESS magnitude.

Appendix C Comparison to the ATL

The apparent underestimation of νmax in the ATL (Fig. 6) means that the calculated detection probability will generally be overestimated, as oscillation amplitudes correlate inversely with νmax. The ATL was built to guide the target selection process for the TESS Asteroseismic Science Consortium (TASC). Though certainly not intentional, the slightly optimistic detection probabilities ensure that a minimal number of targets showing oscillations, if observed, are overlooked in the observing proposals. Conversely, this could explain the generally low fraction of positive seismic detections obtained from TESS, at least early in the mission.

The ATL in its different versions is built on the methodology laid out by Chaplin et al. (2011b), and the ATL3 version (Hey et al. 2024) relies primarily on the Gaia DR3 (Gaia Collaboration 2023) estimates of Teff and log g to calculate νmax following: νmax=gg⊙Teff,⊙Teffνmax,⊙.$\[\nu_{\max }=\frac{g}{g_{\odot}} \sqrt{\frac{T_{\mathrm{eff}, \odot}}{T_{\mathrm{eff}}}} \nu_{\max, \odot}.\]$(C.1)

The Teff (log g) in ATL3 is first and foremost taken as Gaia’s teff_gspphot (logg_gspphot), followed by the Teff (log g) in the TIC (which can originate from a variety of sources; see Stassun et al. 2019) and finally Gaia’s teff_gspspec (logg_gspspec), depending on availability. We note that the detection probability calculation adopted by Lund et al. (2016, 2024) for K2 targets follows the same overall methodology, but here adopting principally 2MASS (J − KS) colours and the relation of Casagrande et al. (2010) to estimate Teff, combined with a luminosity (from Hipparcos) and the stellar mass from a simple mass-luminosity relation to estimate νmax following νmax = M/L (Teff/Teff,⊙)3.5 × νmax,⊙. However, in this case, no systematic offset is seen between predicted and measured νmax values (see their Figure 11 and F.1, respectively), suggesting that the Gaia inputs used to calculate νmax are somehow biased.

To assess the νmax underestimation from the current ATL3 we made a comparison of ATL3 νmax against values from the catalogues of Hatt et al. (2023), Zhou et al. (2024), Lund et al. (2024) (K2; Keystone), and the Kepler targets from Lund et al. (2017), Mathur et al. (2022), and Serenelli et al. (2017). For the K2 and Kepler samples, we have estimates of both Teff and log g (from Teff and νmax) from the above publications, while we for the current TLS adopt the GCS values of Casagrande et al. (2011).

For all samples, we see a general underestimation of νmax from ATL3, ranging between 12 − 20%, and being the most significant for the TLS and K2 samples. For the samples where we have Teff and log g (Kepler, K2, and TLS) in addition to νmax, Fig. C.1 illustrates that a general median underestimation of ~1.2% and ~1.6% is obtained for the Teff and log g values adopted in ATL3 (primarily from Gaia DR3’s gspphot) – this was also noted in the analysis of σ Dra by Hon et al. (2024). For both parameters, especially Teff, we see a tendency for the underestimation to increase with the stellar brightness.

We also compared the offset in νmax for the different possible sources of Teff and log g used in ATL3. Our analysis suggests that for the brighter stars given by the TLS and K2 samples with TESS magnitudes in the approximate range 2.7 − 8.5, a significantly better agreement is obtained by preferentially adopting inputs from the TIC (followed by teff_gspphot/logg_gspphot and lastly teff_gspspec/logg_gspspec) – this order of preferred inputs reduced the νmax underestimation from ~16 − 17% to ~6 − 8% in median. However, for the fainter Kepler sample, typically with TESS magnitudes >8, the current order adopted by the ATL3 does provide the smallest νmax underestimation at ~9% in median.

thumbnail Fig. C.1

Comparison between ATL3 input/output and measured values for the TLS and literature values for the K2 (Lund et al. 2016, 2024) and Kepler (Lund et al. 2017; Mathur et al. 2022; Serenelli et al. 2017) samples. Left: Relative difference (measured-predicted) between measured and predicted values of νmax from ATL3 with its standard priority of inputs on Teff and log g (primarily from Gaia DR3’s gspphot) against TESS magnitude. Middle (Right): Relative difference on Teff (log g) for the three samples. For the K2 and Kepler samples, the measured values are obtained from the literature and adopted from Casagrande et al. (2011) for the TLS. The black markers indicate median binned values of the combined samples.

Appendix D Tables

This appendix provides the tables referred to in the main text of the paper.

Table D.1

TLS global asteroseismic parameters.

Table D.2

TLS stars in the PLATO fields.

Table D.3

TLS exoplanets and sub-stellar objects.

Table D.4

TLS long-baseline interferometry overview.

Table D.5

TLS and binarity.

Appendix E Notes on binarity for individual stars

In addition to the stars listed in Table D.5 we identified 8 stars (θ UMa, η Cas, λ Ser, HD 5015, 14 Boo, v And, HD 46588, and β CVn) that are labeled as spectroscopic binaries on SimBad from appearing in the Seventh Catalogue of Spectroscopic Binary Orbits (SBC7; Batten et al. 1978). In all cases, the discovery reference in SBC7 is to Abt & Levy (1976) where the stars are given as new (first orbit) binary detections. However, nearly all new detections in Abt & Levy (1976) were effectively refuted by Morbey & Griffin (1987) (and acknowledged by Abt (1987)), including all the above cases identified in our sample.

Below, we provide notes on the binarity of individual stars in our sample:

104 Tau (HIP 23835/m Tau) has been intensely studied for binarity and is included in ORB6 with reference to Eggen (1956) who finds two possible period solutions of 1.19 yr and 2.38 yr for the system. However, as discussed in Tokovinin (2012) (see also Heintz & Borgman 1984) the system has remained unresolved in many speckle interferometric studies and found, for example, by Nidever et al. (2002) to be RV stable over a baseline ruling out the previously published period(s).

v And (HIP 7513; Titawin) appears in WDS and is listed in Tokovinin (2014) (see also Lowrance et al. 2002; Raghavan et al. 2010) as a multiple hierarchical system with a wide common proper motion companion (WDS component D) in a > 16.000 yr orbit. v And is the host of at least three confirmed exoplanets (Butler et al. 1997, 1999).

ρ CrB (HIP 78459) is listed in ORB6 with reference to Gatewood et al. (2001) who argued, based on an analysis of Hipparcos and their astrometric data, that the first claimed planetary companion by Noyes et al. (1997) at a period of 39.6 days must instead be a stellar-mass object. However, the significance of this claim was questioned by Zucker & Mazeh (2001) and later refuted by Bender et al. (2005), who was unable to detect the alleged M dwarf companion from high-resolution infrared spectroscopy. Later RV follow-up studies (of which there are many) have currently identified four exoplanets (e.g., Brewer et al. 2023).

κ For (HIP 11072) is a triple star system consisting of a tight binary (radio emitting) M-dwarf pair in a 3.7 day orbit, which orbits the main star with a period of 26.54 ± 0.05 years (Fekel et al. 2018; Tokovinin 2013, and references therein). It is listed on SimBad as being part of the young moving group IC 2391 (Nakajima & Morino 2012), but according to Tokovinin (2013), the calculation of the kinematic parameters leading to this conclusion were biased in that they overlooked the companion.

σ Cet (HIP 11783) is given by McLaughlin (1947) and McLaughlin (1962) as a triple spectroscopic binary, consisting of an A-star pair in a 3.76 day orbit (K1 = K2 = 110 km/s), which orbits the main G-type star at a period of 3.3 yr. We note that the above references are short notes with limited information, and no later or follow-up studies have been identified.

ι Vir (HIP 69701) appears to be part of a hierarchical quadruple system, consisting of two binary pairs in orbit around a common centre of mass. ι Vir (WDS C component) is an astrometric binary with a low-mass companion in an orbit with a preliminary period of ~55 yr (Gontcharov & Kiyaeva 2010). The binary system HIP 69962 (the WDS AB component listed in ORB6 with reference to Videla et al. (2022), and also included in SB9 with a period of ~18.7 yr (Halbwachs et al. 2018)) is a likely wide (ρ = 57.1 arcmin (0.37 pc)) binary component to ι Vir (Shaya & Olling 2011). Fuhrmann & Chini (2015) discuss ι Vir as a possible blue straggler given its higher X-ray luminosity compared to the wide companion.

μ Her (HIP 86974) is a quadruple system consisting of an M-dwarf Ab component orbiting the primary G5IV star at a period of ~99 yr (Roberts et al. 2016), and a faint BC binary component consisting of an M-dwarfs pair with a period of ~43.5 yr (Prieur et al. 2014; Mann et al. 2019). We note that currently, the SONG project has observed μ Her for more than ten years for a detailed asteroseismic characterisation (Grundahl et al. 2017), and these observations can also greatly help to improve the Aa-Ab orbital solution and yield an independent mass constraint (Marcussen et al., in prep.).

94 Cet (HIP 14954) is a triple-star system (Wiegert et al. 2016) consisting of an M-dwarf binary pair with a period of ~1 yr (Roell et al. 2012) in a wide 2029-year-long orbit around 94 Cet A (Roberts et al. 2011). 94 Cet is furthermore a known asteroseismic target (Deal et al. 2017) and an exoplanet host (Mayor et al. 2004) (see also Table D.3).

λ Ara (HIP 86486) is speculated by Fuhrmann et al. (2011b) to be an equal mass binary from the discrepancy between a spectroscopic log g with that of astrometry from Hipparcos. Only vague lower limits on period are provided, and λ Ara has not been included in Table D.5. We note, however, that if we use Eq. C.1 with our measured νmax of 1476 ± 43 μHz and the Teff of 6532 ± 80 K from Fuhrmann et al. (2011b) we obtain log g = 4.15 ± 0.01 dex, which is fully consistent with the Hipparcos log g of 4.1 ± 0.1 dex.

References

  1. ESA 1997, ESA Special Publication, 1200 [Google Scholar]
  2. Abt, H. A. 1987, ApJ, 317, 353 [Google Scholar]
  3. Abt, H. A., & Levy, S. G. 1976, ApJS, 30, 273 [Google Scholar]
  4. Adibekyan, V., Delgado-Mena, E., Feltzing, S., et al. 2017, Astron. Nachr., 338, 442 [NASA ADS] [CrossRef] [Google Scholar]
  5. Agati, J. L., Bonneau, D., Jorissen, A., et al. 2015, A&A, 574, A6 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  6. Ammler-von Eiff, M., & Reiners, A. 2012, A&A, 542, A116 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  7. Antoci, V., Cantiello, M., Khalack, V., et al. 2025, A&A, 696, A111 [Google Scholar]
  8. Appourchaux, T., Michel, E., Auvergne, M., et al. 2008, A&A, 488, 705 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  9. Appourchaux, T., Antia, H. M., Ball, W., et al. 2015, A&A, 582, A25 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  10. Arentoft, T., Kjeldsen, H., Bedding, T. R., et al. 2008, ApJ, 687, 1180 [NASA ADS] [CrossRef] [Google Scholar]
  11. Armstrong, J. T., Mozurkewich, D., Rickard, L. J., et al. 1998, ApJ, 496, 550 [Google Scholar]
  12. Astropy Collaboration (Robitaille, T. P., et al.) 2013, A&A, 558, A33 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  13. Baglin, A., Breger, M., Chevalier, C., et al. 1973, A&A, 23, 221 [NASA ADS] [Google Scholar]
  14. Baglin, A., Auvergne, M., Boisnard, L., et al. 2006, in 36th COSPAR Scientific Assembly, 36, 3749 [Google Scholar]
  15. Baines, E. K., McAlister, H. A., ten Brummelaar, T. A., et al. 2008, ApJ, 680, 728 [NASA ADS] [CrossRef] [Google Scholar]
  16. Baines, E. K., Armstrong, J. T., Schmitt, H. R., et al. 2014, ApJ, 781, 90 [CrossRef] [Google Scholar]
  17. Baines, E. K., Armstrong, J. T., Schmitt, H. R., et al. 2018, AJ, 155, 30 [Google Scholar]
  18. Baines, E. K., Thomas Armstrong, J., Clark, J. H., et al. 2021, AJ, 162, 198 [NASA ADS] [CrossRef] [Google Scholar]
  19. Baines, E. K., Clark, James H., I., Schmitt, H. R., Stone, J. M., & von Braun, K. 2023, AJ, 166, 268 [Google Scholar]
  20. Ball, W. H., Chaplin, W. J., Nielsen, M. B., et al. 2020, MNRAS, 499, 6084 [NASA ADS] [CrossRef] [Google Scholar]
  21. Ball, W. H., Miglio, A., Chaplin, W. J., et al. 2022, MNRAS, 516, 3709 [Google Scholar]
  22. Ballot, J., Gizon, L., Samadi, R., et al. 2011, A&A, 530, A97 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  23. Barban, C., Deheuvels, S., Baudin, F., et al. 2009, A&A, 506, 51 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  24. Barban, C., Deheuvels, S., Goupil, M. J., et al. 2013, in Journal of Physics Conference Series, 440, 012030 [Google Scholar]
  25. Barnes, J. R., Standing, M. R., Haswell, C. A., et al. 2023, MNRAS, 524, 5196 [Google Scholar]
  26. Batten, A. H., Fletcher, J. M., & Mann, P. J. 1978, Publ. Dominion Astrophys. Observ. Victoria, 15, 121 [Google Scholar]
  27. Batten, A. H., Lu, W., & Scarfe, C. D. 1985, JRASC, 79, 167 [Google Scholar]
  28. Bazot, M., Ireland, M. J., Huber, D., et al. 2011, A&A, 526, L4 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  29. Bedding, T. R., Kjeldsen, H., Reetz, J., & Barbuy, B. 1996, MNRAS, 280, 1155 [Google Scholar]
  30. Behr, B. B., Cenko, A. T., Hajian, A. R., et al. 2011, AJ, 142, 6 [Google Scholar]
  31. Bedding, T. R., Butler, R. P., Kjeldsen, H., et al. 2001, ApJ, 549, L105 [NASA ADS] [CrossRef] [Google Scholar]
  32. Bedding, T. R., Butler, R. P., Carrier, F., et al. 2006, ApJ, 647, 558 [NASA ADS] [CrossRef] [Google Scholar]
  33. Benbakoura, M., Gaulme, P., McKeever, J., et al. 2021, A&A, 648, A113 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  34. Bender, C., Simon, M., Prato, L., Mazeh, T., & Zucker, S. 2005, AJ, 129, 402 [Google Scholar]
  35. Benedict, G. F., McArthur, B. E., Bean, J. L., et al. 2010, AJ, 139, 1844 [NASA ADS] [CrossRef] [Google Scholar]
  36. Bergeron, P., Leggett, S. K., & Ruiz, M. T. 2001, ApJS, 133, 413 [Google Scholar]
  37. Bernacca, P. L. & Perinotto, M. 1970, Contrib. Osserv. Astrofis. Univ. Padova Asiago, 239, 1 [Google Scholar]
  38. Bertiau, F. C. 1957, ApJ, 125, 696 [Google Scholar]
  39. Bigot, L., Mourard, D., Berio, P., et al. 2011, A&A, 534, L3 [CrossRef] [EDP Sciences] [Google Scholar]
  40. Bixel, A., & Apai, D. 2020, ApJ, 896, 131 [Google Scholar]
  41. Boden, A. F., Koresko, C. D., van Belle, G. T., et al. 1999, ApJ, 515, 356 [Google Scholar]
  42. Boettner, C., Viswanathan, A., & Dayal, P. 2024, A&A, 692, A150 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  43. Bonanno, A., & Corsaro, E. 2022, ApJ, 939, L26 [NASA ADS] [CrossRef] [Google Scholar]
  44. Bonanno, A., Benatti, S., Claudi, R., et al. 2008, ApJ, 676, 1248 [NASA ADS] [CrossRef] [Google Scholar]
  45. Bonanno, A., Corsaro, E., & Karoff, C. 2014, A&A, 571, A35 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  46. Bond, H. E., Bergeron, P., & Bédard, A. 2017, ApJ, 848, 16 [NASA ADS] [CrossRef] [Google Scholar]
  47. Borisov, S. B., Chilingarian, I. V., Rubtsov, E. V., et al. 2023, ApJS, 266, 11 [NASA ADS] [CrossRef] [Google Scholar]
  48. Borsa, F., Scandariato, G., Rainer, M., et al. 2015, A&A, 578, A64 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  49. Borucki, W. J., Koch, D., Basri, G., et al. 2010, Science, 327, 977 [Google Scholar]
  50. Bouchy, F., & Carrier, F. 2001, A&A, 374, L5 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  51. Bouchy, F., & Carrier, F. 2002, A&A, 390, 205 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  52. Bouchy, F., Bazot, M., Santos, N. C., Vauclair, S., & Sosnowska, D. 2005, A&A, 440, 609 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  53. Boumier, P., Benomar, O., Baudin, F., et al. 2014, A&A, 564, A34 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  54. Bourgés, L., Lafrasse, S., Mella, G., et al. 2014, in Astronomical Data Analysis Software and Systems XXIII, eds. N. Manset, & P. Forshay, Astronomical Society of the Pacific Conference Series, 485, 223 [Google Scholar]
  55. Boyajian, T. S., McAlister, H. A., Baines, E. K., et al. 2008, ApJ, 683, 424 [Google Scholar]
  56. Boyajian, T. S., McAlister, H. A., van Belle, G., et al. 2012a, ApJ, 746, 101 [Google Scholar]
  57. Boyajian, T. S., von Braun, K., van Belle, G., et al. 2012b, ApJ, 757, 112 [Google Scholar]
  58. Boyajian, T. S., von Braun, K., van Belle, G., et al. 2013, ApJ, 771, 40 [Google Scholar]
  59. Brewer, J. M., Zhao, L. L., Fischer, D. A., et al. 2023, AJ, 166, 46 [Google Scholar]
  60. Brogaard, K., Hansen, C. J., Miglio, A., et al. 2018, MNRAS, 476, 3729 [Google Scholar]
  61. Brogaard, K., Arentoft, T., Slumstrup, D., et al. 2022, A&A, 668, A82 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  62. Brown, T. M., Gilliland, R. L., Noyes, R. W., & Ramsey, L. W. 1991, ApJ, 368, 599 [Google Scholar]
  63. Brown, J., Bennum, D., Rodrigue, M., et al. 2000, in American Astronomical Society Meeting Abstracts, 197, 11.03 [Google Scholar]
  64. Bruntt, H., Bedding, T. R., Quirion, P. O., et al. 2010, MNRAS, 405, 1907 [Google Scholar]
  65. Burgasser, A. J., Kirkpatrick, J. D., Cutri, R. M., et al. 2000, ApJ, 531, L57 [Google Scholar]
  66. Butler, R. P., & Marcy, G. W. 1996, ApJ, 464, L153 [NASA ADS] [CrossRef] [Google Scholar]
  67. Butler, R. P., Marcy, G. W., Williams, E., Hauser, H., & Shirts, P. 1997, ApJ, 474, L115 [Google Scholar]
  68. Butler, R. P., Marcy, G. W., Fischer, D. A., et al. 1999, ApJ, 526, 916 [NASA ADS] [CrossRef] [Google Scholar]
  69. Butler, R. P., Tinney, C. G., Marcy, G. W., et al. 2001, ApJ, 555, 410 [NASA ADS] [CrossRef] [Google Scholar]
  70. Campante, T. L., Barclay, T., Swift, J. J., et al. 2015, ApJ, 799, 170 [Google Scholar]
  71. Campante, T. L., Schofield, M., Kuszlewicz, J. S., et al. 2016, ApJ, 830, 138 [Google Scholar]
  72. Campante, T. L., Corsaro, E., Lund, M. N., et al. 2019, ApJ, 885, 31 [Google Scholar]
  73. Campante, T. L., Kjeldsen, H., Li, Y., et al. 2024, A&A, 683, L16 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  74. Carrier, F., & Bourban, G. 2003, A&A, 406, L23 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  75. Carrier, F., & Eggenberger, P. 2006a, Mem. Soc. Astron. Italiana, 77, 326 [Google Scholar]
  76. Carrier, F., & Eggenberger, P. 2006b, A&A, 450, 695 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  77. Carrier, F., Bouchy, F., Kienzle, F., et al. 2001, A&A, 378, 142 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  78. Carrier, F., Eggenberger, P., D’Alessandro, A., & Weber, L. 2005, New A, 10, 315 [Google Scholar]
  79. Carrier, F., Kjeldsen, H., Bedding, T. R., et al. 2007, A&A, 470, 1059 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  80. Casagrande, L., Ramírez, I., Meléndez, J., Bessell, M., & Asplund, M. 2010, A&A, 512, A54 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  81. Casagrande, L., Schönrich, R., Asplund, M., et al. 2011, A&A, 530, A138 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  82. Castelaz, M. W. 1989, AJ, 97, 1184 [Google Scholar]
  83. Castro, M., Baudin, F., Benomar, O., et al. 2021, MNRAS, 505, 2151 [NASA ADS] [CrossRef] [Google Scholar]
  84. Cayrel de Strobel, G. 1996, A&A Rev., 7, 243 [CrossRef] [Google Scholar]
  85. Cayrel de Strobel, G., Perrin, M. N., Cayrel, R., & Lebreton, Y. 1989, A&A, 225, 369 [NASA ADS] [Google Scholar]
  86. Chaplin, W. J., & Miglio, A. 2013, ARA&A, 51, 353 [Google Scholar]
  87. Chaplin, W. J., & Basu, S. 2014, Space Sci. Rev., 186, 437 [NASA ADS] [CrossRef] [Google Scholar]
  88. Chaplin, W. J., Bedding, T. R., Bonanno, A., et al. 2011a, ApJ, 732, L5 [Google Scholar]
  89. Chaplin, W. J., Kjeldsen, H., Bedding, T. R., et al. 2011b, ApJ, 732, 54 [CrossRef] [Google Scholar]
  90. Chelli, A., Duvert, G., Bourgès, L., et al. 2016, A&A, 589, A112 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  91. Choi, J., Dotter, A., Conroy, C., et al. 2016, ApJ, 823, 102 [Google Scholar]
  92. Chontos, A., Huber, D., Berger, T. A., et al. 2021, ApJ, 922, 229 [NASA ADS] [CrossRef] [Google Scholar]
  93. Chontos, A., Huber, D., Sayeed, M., & Yamsiri, P. 2022, J. Open Source Softw., 7, 3331 [NASA ADS] [CrossRef] [Google Scholar]
  94. Corsaro, E., Fröhlich, H. E., Bonanno, A., et al. 2013, MNRAS, 430, 2313 [Google Scholar]
  95. Corsaro, E., McKeever, J. M., & Kuszlewicz, J. S. 2020, A&A, 640, A130 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  96. Corsaro, E., Bonanno, A., Kayhan, C., et al. 2024, A&A, 683, A161 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  97. Creevey, O. L., Monteiro, M. J. P. F. G., Metcalfe, T. S., et al. 2007, ApJ, 659, 616 [Google Scholar]
  98. Cunha, M. S., Aerts, C., Christensen-Dalsgaard, J., et al. 2007, A&A Rev., 14, 217 [Google Scholar]
  99. Cutri, R. M., Skrutskie, M. F., van Dyk, S., et al. 2003, VizieR On-line Data Catalog: II/246 [Google Scholar]
  100. Czesla, S., Schröter, S., Schneider, C. P., et al. 2019, PyA: Python astronomy-related packages [record ascl:1906.010] [Google Scholar]
  101. Datson, J., Flynn, C., & Portinari, L. 2015, A&A, 574, A124 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  102. Davies, G. R., Silva Aguirre, V., Bedding, T. R., et al. 2016, MNRAS, 456, 2183 [Google Scholar]
  103. Deal, M., Escobar, M. E., Vauclair, S., et al. 2017, A&A, 601, A127 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  104. Delrez, L., Ehrenreich, D., Alibert, Y., et al. 2021, Nat. Astron., 5, 775 [NASA ADS] [CrossRef] [Google Scholar]
  105. Desort, M., Lagrange, A. M., Galland, F., et al. 2008, A&A, 491, 883 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  106. Díaz, M. R., Jenkins, J. S., Tuomi, M., et al. 2018, AJ, 155, 126 [CrossRef] [Google Scholar]
  107. Docobo, J. A., Ling, J. F., Prieto, C., et al. 2001, Acta Astron., 51, 353 [Google Scholar]
  108. Docobo, J. A., Ling, J. F., & Campo, P. P. 2012, Catalog of Orbits and Ephemerids of Visual Double Stars, version On-Line (www.usc.es/astro/catalog.htm [Google Scholar]
  109. Dotter, A. 2016, ApJS, 222, 8 [Google Scholar]
  110. Duquennoy, A., & Mayor, M. 1991, A&A, 248, 485 [NASA ADS] [Google Scholar]
  111. Duvert, G. 2016, VizieR On-line Data Catalog: II/345 [Google Scholar]
  112. Egeland, R. 2018, ApJ, 866, 80 [Google Scholar]
  113. Eggen, O. J. 1956, AJ, 61, 405 [Google Scholar]
  114. Eggenberger, A., Udry, S., Chauvin, G., et al. 2007, A&A, 474, 273 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  115. El-Badry, K., Rix, H.-W., & Heintz, T. M. 2021, MNRAS, 506, 2269 [NASA ADS] [CrossRef] [Google Scholar]
  116. Endl, M., Brugamyer, E. J., Cochran, W. D., et al. 2016, ApJ, 818, 34 [NASA ADS] [CrossRef] [Google Scholar]
  117. Epanechnikov, V. A. 1969, Theory Probab. Appl., 14, 153 [Google Scholar]
  118. Eschen, Y. N. E., Bayliss, D., Wilson, T. G., et al. 2024, MNRAS, 535, 1778 [NASA ADS] [CrossRef] [Google Scholar]
  119. Fekel, F. C., Tomkin, J., & Williamson, M. H. 2009, AJ, 137, 3900 [NASA ADS] [CrossRef] [Google Scholar]
  120. Fekel, F. C., Williamson, M., & Pourbaix, D. 2007, AJ, 133, 2431 [Google Scholar]
  121. Fekel, F. C., Williamson, M. H., Muterspaugh, M. W., et al. 2015, AJ, 149, 63 [NASA ADS] [CrossRef] [Google Scholar]
  122. Fekel, F. C., Willmarth, D. W., Abt, H. A., & Pourbaix, D. 2018, AJ, 156, 117 [Google Scholar]
  123. Feltzing, S., & Gonzalez, G. 2001, A&A, 367, 253 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  124. Feng, F., Butler, R. P., Vogt, S. S., et al. 2022, ApJS, 262, 21 [NASA ADS] [CrossRef] [Google Scholar]
  125. Feng, F., Tuomi, M., & Jones, H. R. A. 2017, A&A, 605, A103 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  126. Fischer, D. A., Marcy, G. W., Butler, R. P., et al. 2001, ApJ, 551, 1107 [Google Scholar]
  127. Fischer, D. A., Marcy, G. W., Butler, R. P., Laughlin, G., & Vogt, S. S. 2002, ApJ, 564, 1028 [Google Scholar]
  128. Fischer, D. A., Marcy, G. W., Butler, R. P., et al. 2003, ApJ, 586, 1394 [Google Scholar]
  129. Fischer, D., Driscoll, P., Isaacson, H., et al. 2009, ApJ, 703, 1545 [Google Scholar]
  130. Foellmi, C. 2009, New A, 14, 674 [Google Scholar]
  131. Fontanive, C., Rice, K., Bonavita, M., et al. 2019, MNRAS, 485, 4967 [Google Scholar]
  132. Fuhrmann, K. 2008, MNRAS, 384, 173 [NASA ADS] [CrossRef] [Google Scholar]
  133. Fuhrmann, K., & Chini, R. 2015, ApJ, 809, 107 [NASA ADS] [CrossRef] [Google Scholar]
  134. Fuhrmann, K., Chini, R., Hoffmeister, V. H., & Stahl, O. 2011a, MNRAS, 416, 391 [Google Scholar]
  135. Fuhrmann, K., Chini, R., Hoffmeister, V. H., & Stahl, O. 2011b, MNRAS, 415, 1240 [Google Scholar]
  136. Fuhrmann, K., Chini, R., Haas, M., et al. 2012, ApJ, 761, 159 [NASA ADS] [CrossRef] [Google Scholar]
  137. Fuhrmann, K., Chini, R., Kaderhandt, L., Chen, Z., & Lachaume, R. 2017, MNRAS, 471, 3768 [NASA ADS] [CrossRef] [Google Scholar]
  138. Fulton, B. J., Howard, A. W., Weiss, L. M., et al. 2016, ApJ, 830, 46 [NASA ADS] [Google Scholar]
  139. Gaia Collaboration (Babusiaux, C., et al.) 2018, A&A, 616, A10 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  140. Gaia Collaboration (Vallenari, A., et al.) 2023, A&A, 674, A1 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  141. Galland, F., Lagrange, A. M., Udry, S., et al. 2005, A&A, 444, L21 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  142. Gandolfi, D., Barragán, O., Livingston, J. H., et al. 2018, A&A, 619, L10 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  143. García, R. A., & Ballot, J. 2019, Liv. Rev. Sol. Phys., 16, 4 [Google Scholar]
  144. García, R. A., Mathur, S., Salabert, D., et al. 2010, Science, 329, 1032 [Google Scholar]
  145. Gatewood, G., Han, I., & Black, D. C. 2001, ApJ, 548, L61 [Google Scholar]
  146. Gaulme, P. 2023, https://doi.org/10.5281/zenodo.8107108 [Google Scholar]
  147. Gaulme, P., McKeever, J., Jackiewicz, J., et al. 2016, ApJ, 832, 121 [NASA ADS] [CrossRef] [Google Scholar]
  148. Geballe, T. R., Saumon, D., Leggett, S. K., et al. 2001, ApJ, 556, 373 [Google Scholar]
  149. Ginsburg, A., Sipőcz, B. M., Brasseur, C. E., et al. 2019, AJ, 157, 98 [Google Scholar]
  150. Gliese, W., & Jahreiß, H. 1991, Preliminary Version of the Third Catalogue of Nearby Stars, On: The Astronomical Data Center CD-ROM: Selected Astronomical Catalogs, I, eds. L. E. Brotzmann, & S. E. Gesser, NASA/Astronomical Data Center, Goddard Space Flight Center, Greenbelt, MD [Google Scholar]
  151. Glindemann, A., Bauvir, B., Delplancke, F., et al. 2001, The Messenger, 104, 2 [Google Scholar]
  152. Goldin, A., & Makarov, V. V. 2006, ApJS, 166, 341 [NASA ADS] [CrossRef] [Google Scholar]
  153. Goldin, A., & Makarov, V. V. 2007, ApJS, 173, 137 [NASA ADS] [CrossRef] [Google Scholar]
  154. Gontcharov, G. A., & Kiyaeva, O. V. 2010, New A, 15, 324 [Google Scholar]
  155. Goupil, M. J., Catala, C., Samadi, R., et al. 2024, A&A, 683, A78 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  156. Gregory, P. C., & Fischer, D. A. 2010, MNRAS, 403, 731 [Google Scholar]
  157. Griffin, R. F. 1999, The Observatory, 119, 272 [NASA ADS] [Google Scholar]
  158. Griffin, R. F. 2001, The Observatory, 121, 162 [Google Scholar]
  159. Griffin, R. F. 2011, The Observatory, 131, 225 [Google Scholar]
  160. Grundahl, F., Fredslund Andersen, M., Christensen-Dalsgaard, J., et al. 2017, ApJ, 836, 142 [Google Scholar]
  161. Gullikson, K., Endl, M., Cochran, W. D., & MacQueen, P. J. 2015, ApJ, 815, 62 [NASA ADS] [CrossRef] [Google Scholar]
  162. Guo, Z., Gies, D. R., Matson, R. A., et al. 2017, ApJ, 837, 114 [NASA ADS] [CrossRef] [Google Scholar]
  163. Guzik, J. A., Houdek, G., Chaplin, W. J., et al. 2016, ApJ, 831, 17 [NASA ADS] [CrossRef] [Google Scholar]
  164. Halbwachs, J. L., Mayor, M., & Udry, S. 2012, MNRAS, 422, 14 [NASA ADS] [CrossRef] [Google Scholar]
  165. Halbwachs, J. L., Mayor, M., & Udry, S. 2018, A&A, 619, A81 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  166. Halbwachs, J.-L., Pourbaix, D., Arenou, F., et al. 2023, A&A, 674, A9 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  167. Handberg, R., & Campante, T. L. 2011, A&A, 527, A56 [CrossRef] [EDP Sciences] [Google Scholar]
  168. Handberg, R., & Lund, M. N. 2014, MNRAS, 445, 2698 [Google Scholar]
  169. Hardorp, J. 1982, A&A, 105, 120 [NASA ADS] [Google Scholar]
  170. Hartkopf, W. I., Mason, B. D., & Worley, C. E. 2001a, Sixth Catalog of Orbits of Visual Binary Stars, http://www.ad.usno.navy.mil/wds/orb6/orb6.html [Google Scholar]
  171. Hartkopf, W. I., McAlister, H. A., & Mason, B. D. 2001b, A&A, 122, 3480 [Google Scholar]
  172. Hartkopf, W. I., Tokovinin, A., & Mason, B. D. 2012, AJ, 143, 42 [Google Scholar]
  173. Harvey, J. 1985, in Future Missions in Solar, Heliospheric & Space Plasma Physics, eds. E. Rolfe, & B. Battrick, ESA Special Publication, 235, 199 [Google Scholar]
  174. Hatt, E., Nielsen, M. B., Chaplin, W. J., et al. 2023, A&A, 669, A67 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  175. Hatzes, A. P., Gandolfi, D., Korth, J., et al. 2022, AJ, 163, 223 [NASA ADS] [CrossRef] [Google Scholar]
  176. Heintz, W. D. 1974, AJ, 79, 819 [NASA ADS] [CrossRef] [Google Scholar]
  177. Heintz, W. D., & Borgman, E. R. 1984, AJ, 89, 1068 [Google Scholar]
  178. Hekker, S., Kallinger, T., Baudin, F., et al. 2009, A&A, 506, 465 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  179. Henriksen, A. I., Antoci, V., Saio, H., et al. 2023, MNRAS, 524, 4196 [NASA ADS] [CrossRef] [Google Scholar]
  180. Hey, D., Huber, D., Ong, J., Stello, D., & Foreman-Mackey, D. 2024, arXiv e-prints [arXiv:2403.02489] [Google Scholar]
  181. Hirsch, L. A., Rosenthal, L., Fulton, B. J., et al. 2021, AJ, 161, 134 [NASA ADS] [CrossRef] [Google Scholar]
  182. Hoeg, E., Bässgen, G., Bastian, U., et al. 1997, A&A, 323, L57 [NASA ADS] [Google Scholar]
  183. Hoffleit, D., & Warren, W. H., J. 1995, VizieR On-line Data Catalog: V/50 [Google Scholar]
  184. Hojjatpanah, S., Figueira, P., Santos, N. C., et al. 2019, A&A, 629, A80 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  185. Holberg, J. B., Sion, E. M., Oswalt, T., et al. 2008, AJ, 135, 1225 [NASA ADS] [CrossRef] [Google Scholar]
  186. Holberg, J. B., Oswalt, T. D., Sion, E. M., Barstow, M. A., & Burleigh, M. R. 2013, MNRAS, 435, 2077 [Google Scholar]
  187. Holl, B., Sozzetti, A., Sahlmann, J., et al. 2023, A&A, 674, A10 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  188. Hon, M., Kuszlewicz, J. S., Huber, D., Stello, D., & Reyes, C. 2022, AJ, 164, 135 [CrossRef] [Google Scholar]
  189. Hon, M., Huber, D., Li, Y., et al. 2024, ApJ, 975, 147 [Google Scholar]
  190. Houdek, G., & Dupret, M.-A. 2015, Liv. Rev. Sol. Phys., 12, 8 [Google Scholar]
  191. Hourihane, A., François, P., Worley, C. C., et al. 2023, A&A, 676, A129 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  192. Howell, S. B., Sobeck, C., Haas, M., et al. 2014, PASP, 126, 398 [Google Scholar]
  193. Huber, D. 2018, in Astrophysics and Space Science Proceedings, Vol. 49, Asteroseismology and Exoplanets: Listening to the Stars and Searching for New Worlds, eds. T. L. Campante, N. C. Santos, & M. J. P. F. G. Monteiro, 119 [Google Scholar]
  194. Huber, D., Stello, D., Bedding, T. R., et al. 2009, Communications in Asteroseismology, 160, 74 [Google Scholar]
  195. Huber, D., Bedding, T. R., Stello, D., et al. 2011, ApJ, 743, 143 [Google Scholar]
  196. Huber, D., Ireland, M. J., Bedding, T. R., et al. 2012, ApJ, 760, 32 [Google Scholar]
  197. Huber, D., White, T. R., Metcalfe, T. S., et al. 2022, AJ, 163, 79 [NASA ADS] [CrossRef] [Google Scholar]
  198. Hughes, A. M., Duchêne, G., & Matthews, B. C. 2018, ARA&A, 56, 541 [Google Scholar]
  199. Jannsen, N., De Ridder, J., Seynaeve, D., et al. 2024, A&A, 681, A18 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  200. Jeffers, S. V., Kiefer, R., & Metcalfe, T. S. 2023, Space Sci. Rev., 219, 54 [CrossRef] [Google Scholar]
  201. Jenkins, J. S., Díaz, M., Jones, H. R. A., et al. 2015, MNRAS, 453, 1439 [Google Scholar]
  202. Jenkins, J. M., Twicken, J. D., McCauliff, S., et al. 2016, SPIE Conf. Ser., 9913, 99133E [Google Scholar]
  203. Jiang, C., Bedding, T. R., Stassun, K. G., et al. 2020, ApJ, 896, 65 [Google Scholar]
  204. Jones, H. R. A., Paul Butler, R., Tinney, C. G., et al. 2002, MNRAS, 333, 871 [NASA ADS] [CrossRef] [Google Scholar]
  205. Jönsson, H., Holtzman, J. A., Allende Prieto, C., et al. 2020, AJ, 160, 120 [Google Scholar]
  206. Joyce, M., & Chaboyer, B. 2018, ApJ, 864, 99 [NASA ADS] [CrossRef] [Google Scholar]
  207. Kane, S. R., Boyajian, T. S., Henry, G. W., et al. 2015, ApJ, 806, 60 [Google Scholar]
  208. Kane, S. R., Dalba, P. A., Li, Z., et al. 2019, AJ, 157, 252 [NASA ADS] [CrossRef] [Google Scholar]
  209. Kane, S. R., Yalçınkaya, S., Osborn, H. P., et al. 2020, AJ, 160, 129 [NASA ADS] [CrossRef] [Google Scholar]
  210. Karovicova, I., White, T. R., Nordlander, T., et al. 2022, A&A, 658, A47 [CrossRef] [EDP Sciences] [Google Scholar]
  211. Katoh, N., Itoh, Y., Toyota, E., & Sato, B. 2013, AJ, 145, 41 [Google Scholar]
  212. Kennedy, G. M., Wyatt, M. C., Sibthorpe, B., et al. 2012, MNRAS, 421, 2264 [Google Scholar]
  213. Kervella, P., Thévenin, F., Di Folco, E., & Ségransan, D. 2004, A&A, 426, 297 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  214. Kiefer, R., Schad, A., Davies, G., & Roth, M. 2017, A&A, 598, A77 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  215. Kjeldsen, H., & Bedding, T. R. 1995, A&A, 293, 87 [NASA ADS] [Google Scholar]
  216. Kjeldsen, H., Bedding, T. R., Viskum, M., & Frandsen, S. 1995, AJ, 109, 1313 [NASA ADS] [CrossRef] [Google Scholar]
  217. Kjeldsen, H., Bedding, T. R., Butler, R. P., et al. 2005, ApJ, 635, 1281 [NASA ADS] [CrossRef] [Google Scholar]
  218. Kjeldsen, H., Bedding, T. R., Arentoft, T., et al. 2008, ApJ, 682, 1370 [Google Scholar]
  219. Kjeldsen, H., Bedding, T. R., Li, Y., et al. 2025, A&A, 700, A39 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  220. Konacki, M., Muterspaugh, M. W., Kulkarni, S. R., & Hełminiak, K. G. 2010, ApJ, 719, 1293 [NASA ADS] [CrossRef] [Google Scholar]
  221. Korzennik, S. G., Brown, T. M., Fischer, D. A., Nisenson, P., & Noyes, R. W. 2000, ApJ, 533, L147 [NASA ADS] [CrossRef] [Google Scholar]
  222. Krivov, A. V., Eiroa, C., Löhne, T., et al. 2013, ApJ, 772, 32 [NASA ADS] [CrossRef] [Google Scholar]
  223. Kunkel, W. E., Liebert, J., & Boroson, T. A. 1984, PASP, 96, 891 [Google Scholar]
  224. Li, Y., Bedding, T. R., Li, T., et al. 2018, MNRAS, 476, 470 [Google Scholar]
  225. Li, Y., Huber, D., Ong, J. M. J., et al. 2025, ApJ, 984, 125 [Google Scholar]
  226. Lightkurve Collaboration (Cardoso, J. V. d. M., et al.) 2018, Lightkurve: Kepler and TESS time series analysis in Python, Astrophysics Source Code Library [record ascl:1812.013] [Google Scholar]
  227. Ligi, R., Mourard, D., Lagrange, A. M., et al. 2012, A&A, 545, A5 [Google Scholar]
  228. Ligi, R., Creevey, O., Mourard, D., et al. 2016, A&A, 586, A94 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  229. Loutrel, N. P., Luhman, K. L., Lowrance, P. J., & Bochanski, J. J. 2011, ApJ, 739, 81 [NASA ADS] [CrossRef] [Google Scholar]
  230. Lowrance, P. J., Kirkpatrick, J. D., & Beichman, C. A. 2002, ApJ, 572, L79 [NASA ADS] [CrossRef] [Google Scholar]
  231. Luck, R. E. 2017, AJ, 153, 21 [Google Scholar]
  232. Lund, M. N., Handberg, R., Davies, G. R., Chaplin, W. J., & Jones, C. D. 2015, ApJ, 806, 30 [NASA ADS] [CrossRef] [Google Scholar]
  233. Lund, M. N., Chaplin, W. J., Casagrande, L., et al. 2016, PASP, 128, 124204 [CrossRef] [Google Scholar]
  234. Lund, M. N., Silva Aguirre, V., Davies, G. R., et al. 2017, ApJ, 835, 172 [Google Scholar]
  235. Lund, M. N., Knudstrup, E., Silva Aguirre, V., et al. 2019, AJ, 158, 248 [CrossRef] [Google Scholar]
  236. Lund, M. N., Basu, S., Bieryla, A., et al. 2024, A&A, 688, A13 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  237. Lundkvist, M. S., Huber, D., Silva Aguirre, V., & Chaplin, W. J. 2018, arXiv e-prints [arXiv:1804.02214] [Google Scholar]
  238. Lundkvist, M. S., Kjeldsen, H., Bedding, T. R., et al. 2024, ApJ, 964, 110 [NASA ADS] [CrossRef] [Google Scholar]
  239. Maestro, V., Che, X., Huber, D., et al. 2013, MNRAS, 434, 1321 [NASA ADS] [CrossRef] [Google Scholar]
  240. Malla, S. P., Stello, D., Montet, B. T., et al. 2024, MNRAS, 534, 1775 [Google Scholar]
  241. Mamajek, E., & Stapelfeldt, K. 2024, arXiv e-prints [arXiv:2402.12414] [Google Scholar]
  242. Mann, A. W., Dupuy, T., Kraus, A. L., et al. 2019, ApJ, 871, 63 [Google Scholar]
  243. Marcy, G. W., & Butler, R. P. 1996, ApJ, 464, L147 [NASA ADS] [CrossRef] [Google Scholar]
  244. Marshall, J. P., Krivov, A. V., del Burgo, C., et al. 2013, A&A, 557, A58 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  245. Martić, M., Lebrun, J. C., Schmitt, J., Appourchaux, T., & Bertaux, J. L. 2001, in SOHO 10/GONG 2000 Workshop: Helio- and Asteroseismology at the Dawn of the Millennium, eds. A. Wilson, & P. L. Pallé, ESA Special Publication, Vol. 464, 431 [Google Scholar]
  246. Martić, M., Schmitt, J., Lebrun, J. C., et al. 1999, A&A, 351, 993 [Google Scholar]
  247. Martínez-Arnáiz, R., Maldonado, J., Montes, D., Eiroa, C., & Montesinos, B. 2010, A&A, 520, A79 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  248. Mason, B. D., Wycoff, G. L., Hartkopf, W. I., Douglass, G. G., & Worley, C. E. 2014, VizieR On-line Data Catalog: B/wds [Google Scholar]
  249. Mason, B. D., Hartkopf, W. I., & Miles, K. N. 2017, AJ, 154, 200 [Google Scholar]
  250. Mathur, S., García, R. A., Catala, C., et al. 2010a, A&A, 518, A53 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  251. Mathur, S., García, R. A., Régulo, C., et al. 2010b, A&A, 511, A46 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  252. Mathur, S., Bruntt, H., Catala, C., et al. 2013, A&A, 549, A12 [CrossRef] [EDP Sciences] [Google Scholar]
  253. Mathur, S., García, R. A., Bugnet, L., et al. 2019, Front. Astron. Space Sci., 6, 46 [Google Scholar]
  254. Mathur, S., García, R. A., Breton, S., et al. 2022, A&A, 657, A31 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  255. Maxted, P. F. L., Bloemen, S., Heber, U., et al. 2014, MNRAS, 437, 1681 [NASA ADS] [CrossRef] [Google Scholar]
  256. Mayor, M., & Queloz, D. 1995, Nature, 378, 355 [Google Scholar]
  257. Mayor, M., Udry, S., Naef, D., et al. 2004, A&A, 415, 391 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  258. Mazeh, T., Prato, L., Simon, M., et al. 2002, ApJ, 564, 1007 [Google Scholar]
  259. McCarthy, C., Butler, R. P., Tinney, C. G., et al. 2004, ApJ, 617, 575 [Google Scholar]
  260. McCook, G. P., & Sion, E. M. 2016, VizieR On-line Data Catalog: B/wd [Google Scholar]
  261. McLaughlin, D. B. 1947, ApJ, 105, 497 [Google Scholar]
  262. McLaughlin, D. B. 1962, AJ, 67, 117 [Google Scholar]
  263. Mennesson, B., Millan-Gabet, R., Serabyn, E., et al. 2014, ApJ, 797, 119 [Google Scholar]
  264. Metcalfe, T. S., Chaplin, W. J., Appourchaux, T., et al. 2012, ApJ, 748, L10 [Google Scholar]
  265. Metcalfe, T. S., Creevey, O. L., & Davies, G. R. 2015, ApJ, 811, L37 [NASA ADS] [CrossRef] [Google Scholar]
  266. Metcalfe, T. S., van Saders, J. L., Basu, S., et al. 2020, ApJ, 900, 154 [Google Scholar]
  267. Metcalfe, T. S., van Saders, J. L., Basu, S., et al. 2021, ApJ, 921, 122 [NASA ADS] [CrossRef] [Google Scholar]
  268. Metcalfe, T. S., Buzasi, D., Huber, D., et al. 2023, AJ, 166, 167 [CrossRef] [Google Scholar]
  269. Metcalfe, T. S., van Saders, J. L., Huber, D., et al. 2024, ApJ, 974, 31 [Google Scholar]
  270. Michel, E., Baglin, A., Auvergne, M., et al. 2008, Science, 322, 558 [Google Scholar]
  271. Miglio, A., Chaplin, W. J., Farmer, R., et al. 2014, ApJ, 784, L3 [CrossRef] [Google Scholar]
  272. Mirabel, I. F., Mignani, R., Rodrigues, I., et al. 2002, A&A, 395, 595 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  273. Mirouh, G. M., Reese, D. R., Lara, F. E., Ballot, J., & Rieutord, M. 2014, in Precision Asteroseismology, eds. J. A. Guzik, W. J. Chaplin, G. Handler, & A. Pigulski, IAU Symposium, 301, 455 [Google Scholar]
  274. Mishenina, T. V. 1996, A&AS, 119, 321 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  275. Monnier, J. D., Townsend, R. H. D., Che, X., et al. 2010, ApJ, 725, 1192 [NASA ADS] [CrossRef] [Google Scholar]
  276. Montalto, M., Piotto, G., Marrese, P. M., et al. 2021, A&A, 653, A98 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  277. Montes, D., González-Peinado, R., Tabernero, H. M., et al. 2018, MNRAS, 479, 1332 [Google Scholar]
  278. Montesinos, B., Eiroa, C., Krivov, A. V., et al. 2016, A&A, 593, A51 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  279. Morbey, C. L., & Griffin, R. F. 1987, ApJ, 317, 343 [Google Scholar]
  280. Morel, P., Morel, C., Provost, J., & Berthomieu, G. 2000, A&A, 354, 636 [Google Scholar]
  281. Mosser, B., Bouchy, F., Catala, C., et al. 2005, A&A, 431, L13 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  282. Mosser, B., Deheuvels, S., Michel, E., et al. 2008, A&A, 488, 635 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  283. Mosser, B., Michel, E., Appourchaux, T., et al. 2009, A&A, 506, 33 [CrossRef] [EDP Sciences] [Google Scholar]
  284. Mozurkewich, D., Armstrong, J. T., Hindsley, R. B., et al. 2003, AJ, 126, 2502 [NASA ADS] [CrossRef] [Google Scholar]
  285. Mugrauer, M. 2019, MNRAS, 490, 5088 [Google Scholar]
  286. Mugrauer, M., Neuhäuser, R., Mazeh, T., Guenther, E., & Fernández, M. 2004, Astron. Nachr., 325, 718 [NASA ADS] [CrossRef] [Google Scholar]
  287. Murdoch, K., & Hearnshaw, J. B. 1991, Ap&SS, 186, 169 [Google Scholar]
  288. Murdoch, K., & Hearnshaw, J. B. 1993, The Observatory, 113, 126 [NASA ADS] [Google Scholar]
  289. Naef, D., Mayor, M., Korzennik, S. G., et al. 2003, A&A, 410, 1051 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  290. Nakajima, T., & Morino, J.-I. 2012, AJ, 143, 2 [Google Scholar]
  291. Nari, N., Dumusque, X., Hara, N. C., et al. 2025, A&A, 693, A297 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  292. Nascimbeni, V., Piotto, G., Börner, A., et al. 2022, A&A, 658, A31 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  293. Nascimbeni, V., Piotto, G., Cabrera, J., et al. 2025, A&A, 694, A313 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  294. Neubauer, F. J. 1929, PASP, 41, 371 [Google Scholar]
  295. Nidever, D. L., Marcy, G. W., Butler, R. P., Fischer, D. A., & Vogt, S. S. 2002, ApJS, 141, 503 [Google Scholar]
  296. Nissen, P. E., Christensen-Dalsgaard, J., Mosumgaard, J. R., et al. 2020, A&A, 640, A81 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  297. Nielsen, M. B., Ball, W. H., Standing, M. R., et al. 2020, A&A, 641, A25 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  298. Nielsen, M. B., Davies, G. R., Ball, W. H., et al. 2021, AJ, 161, 62 [NASA ADS] [CrossRef] [Google Scholar]
  299. Nissen, P. E., & Schuster, W. J. 2011, A&A, 530, A15 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  300. Nordgren, T. E., Germain, M. E., Benson, J. A., et al. 1999, AJ, 118, 3032 [NASA ADS] [CrossRef] [Google Scholar]
  301. Nordgren, T. E., Sudol, J. J., & Mozurkewich, D. 2001, AJ, 122, 2707 [NASA ADS] [CrossRef] [Google Scholar]
  302. Nordström, B., Mayor, M., Andersen, J., et al. 2004, A&A, 418, 989 [Google Scholar]
  303. North, J. R., Davis, J., Bedding, T. R., et al. 2007, MNRAS, 380, L80 [Google Scholar]
  304. Noyes, R. W., Jha, S., Korzennik, S. G., et al. 1997, ApJ, 483, L111 [NASA ADS] [CrossRef] [Google Scholar]
  305. Odland, T. 2018, tommyod/KDEpy: Kernel Density Estimation in Python [Google Scholar]
  306. Ong, J. M. J., Basu, S., Lund, M. N., et al. 2021, ApJ, 922, 18 [NASA ADS] [CrossRef] [Google Scholar]
  307. Orlando, S., Favata, F., Micela, G., et al. 2017, A&A, 605, A19 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  308. Orosz, J. A., & Bailyn, C. D. 1997, ApJ, 477, 876 [Google Scholar]
  309. O’Toole, S., Tinney, C. G., Butler, R. P., et al. 2009, ApJ, 697, 1263 [Google Scholar]
  310. Paegert, M., Stassun, K. G., Collins, K. A., et al. 2021, arXiv e-prints [arXiv:2108.04778] [Google Scholar]
  311. Paunzen, E., Hümmerich, S., Bernhard, K., & Walczak, P. 2017, MNRAS, 468, 2017 [NASA ADS] [CrossRef] [Google Scholar]
  312. Pearce, T. D. 2024, arXiv e-prints [arXiv:2403.11804] [Google Scholar]
  313. Pepe, F., Correia, A. C. M., Mayor, M., et al. 2007, A&A, 462, 769 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  314. Pepe, F., Lovis, C., Ségransan, D., et al. 2011, A&A, 534, A58 [CrossRef] [EDP Sciences] [Google Scholar]
  315. Perryman, M. A. C., Lindegren, L., Kovalevsky, J., et al. 1997, A&A, 323, L49 [Google Scholar]
  316. Pijpers, F. P., Teixeira, T. C., Garcia, P. J., et al. 2003, A&A, 406, L15 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  317. Pope, B. J. S., White, T. R., Farr, W. M., et al. 2019, ApJS, 245, 8 [NASA ADS] [CrossRef] [Google Scholar]
  318. Porto de Mello, G. F., da Silva, R., da Silva, L., & de Nader, R. V. 2014, A&A, 563, A52 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  319. Pourbaix, D. 2000, A&AS, 145, 215 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  320. Pourbaix, D., Tokovinin, A. A., Batten, A. H., et al. 2004, A&A, 424, 727 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  321. Prieur, J. L., Scardia, M., Pansecchi, L., et al. 2014, Astron. Nachr., 335, 817 [Google Scholar]
  322. Raghavan, D., Henry, T. J., Mason, B. D., et al. 2006, ApJ, 646, 523 [NASA ADS] [CrossRef] [Google Scholar]
  323. Raghavan, D., McAlister, H. A., Henry, T. J., et al. 2010, ApJS, 190, 1 [Google Scholar]
  324. Rains, A. D., Ireland, M. J., White, T. R., Casagrande, L., & Karovicova, I. 2020, MNRAS, 493, 2377 [Google Scholar]
  325. Ramírez, I., Meléndez, J., & Asplund, M. 2009, A&A, 508, L17 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  326. Ramírez, I., Allende Prieto, C., & Lambert, D. L. 2013, ApJ, 764, 78 [CrossRef] [Google Scholar]
  327. Rappaport, S., Podsiadlowski, P., Joss, P. C., Di Stefano, R., & Han, Z. 1995, MNRAS, 273, 731 [NASA ADS] [CrossRef] [Google Scholar]
  328. Rauer, H., Aerts, C., Cabrera, J., et al. 2025, Exp. Astron., 59, 26 [Google Scholar]
  329. Ren, S., & Fu, Y. 2013, AJ, 145, 81 [NASA ADS] [CrossRef] [Google Scholar]
  330. Rhee, J. H., Song, I., Zuckerman, B., & McElwain, M. 2007, ApJ, 660, 1556 [Google Scholar]
  331. Ricker, G. R., Winn, J. N., Vanderspek, R., et al. 2014, SPIE Conf. Ser., 9143, 914320 [Google Scholar]
  332. Roberts, L. C.Jr, Turner, N. H., ten Brummelaar, T. A., Mason, B. D., & Hartkopf, W. I. 2011, AJ, 142, 175 [NASA ADS] [CrossRef] [Google Scholar]
  333. Roberts, L. C.Jr, Mason, B. D., Aguilar, J., et al. 2016, AJ, 151, 169 [NASA ADS] [CrossRef] [Google Scholar]
  334. Rodriguez, D. R., & Zuckerman, B. 2012, ApJ, 745, 147 [Google Scholar]
  335. Roell, T., Neuhäuser, R., Seifahrt, A., & Mugrauer, M. 2012, A&A, 542, A92 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  336. Rosenthal, L. J., Fulton, B. J., Hirsch, L. A., et al. 2021, ApJS, 255, 8 [NASA ADS] [CrossRef] [Google Scholar]
  337. Šubjak, J., Lodieu, N., Kabáth, P., et al. 2023, A&A, 671, A10 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  338. Salabert, D., Régulo, C., García, R. A., et al. 2016, A&A, 589, A118 [CrossRef] [EDP Sciences] [Google Scholar]
  339. Samus’, N. N., Kazarovets, E. V., Durlevich, O. V., Kireeva, N. N., & Pastukhova, E. N. 2017, Astron. Rep., 61, 80 [Google Scholar]
  340. Santos, N. C., Bouchy, F., Mayor, M., et al. 2004, A&A, 426, L19 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  341. Santos, A. R. G., Campante, T. L., Chaplin, W. J., et al. 2018, ApJS, 237, 17 [NASA ADS] [CrossRef] [Google Scholar]
  342. Santos, A. R. G., Campante, T. L., Chaplin, W. J., et al. 2019, ApJ, 883, 65 [CrossRef] [Google Scholar]
  343. Sayeed, M., Huber, D., Chontos, A., & Li, Y. 2025, arXiv e-prints [arXiv:2503.15599] [Google Scholar]
  344. Schaefer, G. H., White, R. J., Baines, E. K., et al. 2018, ApJ, 858, 71 [Google Scholar]
  345. Schnupp, C., Bergfors, C., Brandner, W., et al. 2010, A&A, 516, A21 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  346. Schofield, M., Chaplin, W. J., Huber, D., et al. 2019, ApJS, 241, 12 [Google Scholar]
  347. Schröder, C., Reiners, A., & Schmitt, J. H. M. M. 2009, A&A, 493, 1099 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  348. Schultz, A., Lisse, C., Brown, J., et al. 2014, unpublished manuscript. Available at: https://www.researchgate.net/publication/237119578_Discovery_of_Three_Faint_Brown_Dwarfs_with_HSTNICMOS [Google Scholar]
  349. Serenelli, A., Johnson, J., Huber, D., et al. 2017, ApJS, 233, 23 [Google Scholar]
  350. Serenelli, A., Weiss, A., Aerts, C., et al. 2021, A&A Rev., 29, 4 [NASA ADS] [CrossRef] [Google Scholar]
  351. Shaya, E. J., & Olling, R. P. 2011, ApJS, 192, 2 [CrossRef] [Google Scholar]
  352. Skuljan, J., Ramm, D. J., & Hearnshaw, J. B. 2004, MNRAS, 352, 975 [Google Scholar]
  353. Smallwood, J. L., Franchini, A., Chen, C., et al. 2020, MNRAS, 494, 487 [NASA ADS] [CrossRef] [Google Scholar]
  354. Soderblom, D. R., & King, J. R. 1998, in Solar Analogs: Characteristics and Optimum Candidates, ed. J. C. Hall, 41 [Google Scholar]
  355. Soubiran, C., Brouillet, N., & Casamiquela, L. 2022, A&A, 663, A4 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  356. Spencer Jones, H. 1928, Ann. Cape Observ., 10, 8 [Google Scholar]
  357. Sreenivas, K. R., Bedding, T. R., Huber, D., et al. 2025, MNRAS, 537, 3265 [Google Scholar]
  358. Stassun, K. G., Oelkers, R. J., Paegert, M., et al. 2019, AJ, 158, 138 [Google Scholar]
  359. Stello, D., Chaplin, W. J., Basu, S., Elsworth, Y., & Bedding, T. R. 2009, MNRAS, 400, L80 [Google Scholar]
  360. Strassmeier, K. G., Weber, M., Gruner, D., et al. 2023, A&A, 671, A7 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  361. Takeda, Y., Sato, B., Kambe, E., et al. 2005, PASJ, 57, 13 [NASA ADS] [Google Scholar]
  362. Tautvaišienė, G., Mikolaitis, Š., Drazdauskas, A., et al. 2020, ApJS, 248, 19 [Google Scholar]
  363. Tautvaišienė, G., Mikolaitis, S., Drazdauskas, A., et al. 2022, ApJS, 259, 45 [CrossRef] [Google Scholar]
  364. Teixeira, T. C., Kjeldsen, H., Bedding, T. R., et al. 2009, A&A, 494, 237 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  365. ten Brummelaar, T. A., McAlister, H. A., Ridgway, S. T., et al. 2005, ApJ, 628, 453 [Google Scholar]
  366. Thévenin, F., Kervella, P., Pichon, B., et al. 2005, A&A, 436, 253 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  367. Thomsen, J. S., Brogaard, K., Arentoft, T., et al. 2022, MNRAS, 517, 4187 [NASA ADS] [CrossRef] [Google Scholar]
  368. Tinney, C. G., Butler, R. P., Marcy, G. W., et al. 2002, ApJ, 571, 528 [NASA ADS] [CrossRef] [Google Scholar]
  369. Tinney, C. G., Butler, R. P., Jones, H. R. A., et al. 2011, ApJ, 727, 103 [Google Scholar]
  370. Tokovinin, A. 2012, AJ, 144, 56 [Google Scholar]
  371. Tokovinin, A. 2013, AJ, 145, 76 [Google Scholar]
  372. Tokovinin, A. 2014, AJ, 147, 86 [Google Scholar]
  373. Trilling, D. E., Bryden, G., Beichman, C. A., et al. 2008, ApJ, 674, 1086 [Google Scholar]
  374. Tuchow, N. W., Stark, C. C., & Mamajek, E. 2024, AJ, 167, 139 [NASA ADS] [CrossRef] [Google Scholar]
  375. Udry, S., Dumusque, X., Lovis, C., et al. 2019, A&A, 622, A37 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  376. Uytterhoeven, K., Mathias, P., Baglin, A., et al. 2011, arXiv e-prints [arXiv:1111.1840] [Google Scholar]
  377. van Biesbroeck, G. 1961, AJ, 66, 528 [NASA ADS] [CrossRef] [Google Scholar]
  378. van der Walt, S., Schönberger, J. L., Nunez-Iglesias, J., et al. 2014, PeerJ, 2, e453 [Google Scholar]
  379. Van Eylen, V., Lund, M. N., Silva Aguirre, V., et al. 2014, ApJ, 782, 14 [NASA ADS] [CrossRef] [Google Scholar]
  380. van Leeuwen, F., Evans, D. W., Grenon, M., et al. 1997, A&A, 323, L61 [NASA ADS] [Google Scholar]
  381. Vanderspek, R., Doty, J. P., Fausnaugh, M., et al. 2018, TESS Instrument Handbook, version 0.1 [Google Scholar]
  382. Vauclair, S., Laymand, M., Bouchy, F., et al. 2008, A&A, 482, L5 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  383. Verner, G. A., & Roxburgh, I. W. 2011, arXiv e-prints [arXiv:1104.0631] [Google Scholar]
  384. Verner, G. A., Elsworth, Y., Chaplin, W. J., et al. 2011, MNRAS, 415, 3539 [NASA ADS] [CrossRef] [Google Scholar]
  385. Venner, A., Vanderburg, A., & Pearce, L. A. 2021, AJ, 162, 12 [NASA ADS] [CrossRef] [Google Scholar]
  386. Viani, L. S., Basu, S., Corsaro, E., Ball, W. H., & Chaplin, W. J. 2019, ApJ, 879, 33 [NASA ADS] [CrossRef] [Google Scholar]
  387. Videla, M., Mendez, R. A., Clavería, R. M., Silva, J. F., & Orchard, M. E. 2022, AJ, 163, 220 [NASA ADS] [CrossRef] [Google Scholar]
  388. Vinícius, Z., Barentsen, G., Hedges, C., Gully-Santiago, M., & Cody, A. M. 2018, http://doi.org/10.5281/zenodo.1181928 [Google Scholar]
  389. Vogt, S. S., Butler, R. P., Marcy, G. W., et al. 2005, ApJ, 632, 638 [Google Scholar]
  390. Vogt, S. S., Wittenmyer, R. A., Butler, R. P., et al. 2010, ApJ, 708, 1366 [NASA ADS] [CrossRef] [Google Scholar]
  391. von Braun, K., Boyajian, T. S., van Belle, G. T., et al. 2014, MNRAS, 438, 2413 [CrossRef] [Google Scholar]
  392. Vrard, M., Mourard, D., Creevey, O., et al. 2024, in 8th TESS/15th Kepler Asteroseismic Science Consortium Workshop, 12 [Google Scholar]
  393. Weeks, A., Huber, D., Stokholm, A., et al. 2025, MNRAS, 541, 2459 [Google Scholar]
  394. Wenger, M., Ochsenbein, F., Egret, D., et al. 2000, A&AS, 143, 9 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  395. Wesemael, F., Greenstein, J. L., Liebert, J., et al. 1993, PASP, 105, 761 [NASA ADS] [CrossRef] [Google Scholar]
  396. White, T. R., Huber, D., Maestro, V., et al. 2013, MNRAS, 433, 1262 [Google Scholar]
  397. White, T. R., Benomar, O., Silva Aguirre, V., et al. 2017, A&A, 601, A82 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  398. White, T. R., Huber, D., Mann, A. W., et al. 2018, MNRAS, 477, 4403 [NASA ADS] [CrossRef] [Google Scholar]
  399. Wiegert, J., Faramaz, V., & Cruz-Saenz de Miera, F. 2016, MNRAS, 462, 1735 [Google Scholar]
  400. Wilson, J. C., Kirkpatrick, J. D., Gizis, J. E., et al. 2001, AJ, 122, 1989 [NASA ADS] [CrossRef] [Google Scholar]
  401. Wittenmyer, R. A., Horner, J., Tuomi, M., et al. 2012, ApJ, 753, 169 [CrossRef] [Google Scholar]
  402. Wittenmyer, R. A., Clark, J. T., Zhao, J., et al. 2019, MNRAS, 484, 5859 [NASA ADS] [CrossRef] [Google Scholar]
  403. Wyatt, M. C., Kennedy, G., Sibthorpe, B., et al. 2012, MNRAS, 424, 1206 [NASA ADS] [CrossRef] [Google Scholar]
  404. Xing, K., Zong, W., Silvotti, R., et al. 2024, ApJS, 271, 57 [CrossRef] [Google Scholar]
  405. Zechmeister, M., Kürster, M., Endl, M., et al. 2013, A&A, 552, A78 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  406. Zhao, M., Monnier, J. D., Che, X., et al. 2011, PASP, 123, 964 [NASA ADS] [CrossRef] [Google Scholar]
  407. Zhou, J., Bi, S., Yu, J., et al. 2024, ApJS, 271, 17 [Google Scholar]
  408. Zucker, S., & Mazeh, T. 2001, arXiv e-prints [arXiv:0104098] [Google Scholar]
1

The Python module mundey (https://github.com/hvidy/mundey) by White & Pope could possibly achieve this.

2

Using the conservative criterion of a detection probability of Pdet ≥ 50%.

3

Available via the Python module tess-atl (v0.1) at https://github.com/danhey/tess-atl

5

This fraction is a heuristic estimate based on previous experience with analysis of comparable Kepler/K2 data.

7

The PLATO field plots are made using functionalities of the platopoint code by Hugh P. Orborn, in the modified version used by Boettner et al. (2024) (https://github.com/ChrisBoettner/plato/tree/main/plato/instrument/platopoint.py).

8

HD 131977, 18 Sco, 36 Oph A+B, ξ Oph, 58 Oph, and 70 Oph A+B.

12

HIPs 39903, 69226, 80686, 37606, 86036, 5081, and 50319.

13

From the conversion of the provided Thiele-Innes elements (see Halbwachs et al. 2023, Appendix A).

14

We note that upon joining the SONG community (https://soda.phys.au.dk/), the spectra in the SONG database are freely available for members to use.

16

Using the isochrones Python module (https://github.com/timothydmorton/isochrones).

17

to Trekkies, potentially better known as the planet Vulcan’s host star in the Star Trek universe.

18

The SPOC outlier near Tmag~4.75 is TIC 20601206 (HIP 93017) which for several sectors in 120-s cadence data have an aperture defined by a few disjoint pixels. For the corresponding 20-s cadence data, the apertures look as expected.

All Tables

Table A.1

Stars fulfilling TLS selection criteria, but with no data before Sector 78.

In the text
Table D.1

TLS global asteroseismic parameters.

In the text
Table D.2

TLS stars in the PLATO fields.

In the text
Table D.3

TLS exoplanets and sub-stellar objects.

In the text
Table D.4

TLS long-baseline interferometry overview.

In the text
Table D.5

TLS and binarity.

In the text

All Figures

thumbnail Fig. 1

HR-diagram showing the criteria for our selection of targets. The red line shows our limits on MV and (B − V), and targets in the lower non-shaded region were selected for analysis. The marker colour indicates the V-band magnitude for the stars, while the marker size indicates the number of sectors TESS will have observed a given star in Cycle 6 (up to and including Sector 83). The stars in the top left shaded box are not expected to show solar-like oscillations and here we have not indicated V nor the number of sectors. The stars in the top-right shaded region contain evolved stars that may well show solar-like oscillations – these will be the subject of a future study.
In the text
thumbnail Fig. 2

Example of the effect of adopting custom apertures, here for ψ1 Dra A (TIC 441804568) as observed during Sector 15 in 120-s cadence, where the SPOC aperture is missing several high-flux pixels. Similar apertures are seen for ψ1 Dra A in other sectors. Left: adopted aperture, shown with a black outline, combining the SPOC aperture, in orange, and the K2P2 aperture, in red. The green outline shows the aperture used to estimate the background. In blue, the median pixel flux levels are shown on a log-scale. Right: segments of the power-density spectra of the filtered SAP light curves from the apertures on the left, where the PSD obtained from the SPOC aperture is shown in orange and the one from the adopted custom aperture is shown in red. The inset shows a zoom of the region with identified oscillations from the custom aperture data. The small inset to the right shows the échelle diagram of the zoomed region after correcting for the background using a robust Siegel slope estimator.
In the text
thumbnail Fig. 3

Examples of PSD for a small subset of stars with detected oscillations, arranged according to increasing νmax (see Table D.1 for details). The spectra have been smoothed by an Epanechnikov kernel (Epanechnikov 1969) with a width of Δν/20.
In the text
thumbnail Fig. 4

Comparison of the TLS with solar-like detections in MS/SG stars from other missions. Left: position of solar-like oscillators in the HR-diagram, with an indication of the selection criteria in MV and (B − V) used to define our sample (Sect. 2). The marker size indicates the V-band magnitude of the stars, while the marker edge colour indicates how or by which mission oscillations were first detected. Any stars with a detection of oscillations from this work are shown with a filled yellow marker. Stars with ground-based detections were identified from individual cases in the literature (see Table D.1 and Sect. 5.1); the Kepler comparison sample was constructed from the compilations of Lund et al. (2017), Serenelli et al. (2017), Mathur et al. (2022), in addition to Kepler-444 (Campante et al. 2015) and θ Cyg (Guzik et al. 2016); the nine stars from CoRoT were identified from individual cases in the literature (Barban et al. 2009, 2013; Appourchaux et al. 2008; Mosser et al. 2009; Mathur et al. 2010a, 2013; Ballot et al. 2011; Boumier et al. 2014; Castro et al. 2021); the stars forming the K2 sample are obtained from Lund et al. (2016, 2024); while the TESS sample was obtained from the catalogues of Hatt et al. (2023), Zhou et al. (2024), and Corsaro et al. (2024, considering only their confident detections; their Table 1), in addition to individual cases from the literature (see Table D.1). For the TESS and K2 comparison samples, we have limited these to stars with νmax < 284 μHz. Right: distribution of the stars in terms of distance and νmax, using only stars that in the left plot fall within the MV and (B − V) boundaries defined in our target selection. We note that α Cen A+B, at a distance of ~1.35 pc, have been omitted from the plot. Distances and magnitudes used in this plot were adopted from the TESS Input Catalog (TICv8.2; Paegert et al. 2021). The horizontal dashed line indicates the solar νmax for comparison.
In the text
thumbnail Fig. 5

Left: correlation between the measured global asteroseismic parameters Δν and νmax. The dashed line indicates the empirical relation from Huber et al. (2011) together with the 1- and 2-σ confidence bands on their relation. Right: KDE of the relative uncertainties on Δν and νmax for the sample, with median values of ~1.6% in Δν and ~3.7% in νmax. The ticks at the bottom of the panel indicate the individual values, coloured according to the legend.
In the text
thumbnail Fig. 6

Comparison of νmax values for the stars in our sample that overlap with those of Hatt et al. (2023), Zhou et al. (2024), and Corsaro et al. (2024, considering only their confident detections; their Table 1), with the predictions from the Asteroseismic Target List (ATL) versions 1 (Schofield et al. 2019) and 3 (Hey et al. 2024). Left: direct comparison between our values and those published in the literature or predicted in the ATLs. The colour indicates the comparison source (see legend), while the numbers in the legend indicate how many stars are in common with the different comparison sources. Middle: relative differences between the values. Values beyond either +30% or −60% have been adjusted to these values (dotted lines) for a better visual rendition. Right: KDE of the relative differences.
In the text
thumbnail Fig. 7

Comparison of noise statistics between different light curve sources and observing cadences, including all stars with 20-s cadence observations where a custom aperture was constructed (cf. Sect. 3). Top left: RMSD of 20-s light curve flux binned to 1 hour against TESS magnitude for both SPOC and custom aperture data. Top right: P2P-MDV of 20-s light curve flux binned to 120-s against TESS magnitude. Bottom left: ratio between the 1-hour RMSD from 20-s and 120-s cadence custom aperture data. The red markers indicate median-binned values, with uncertainties given by the standardised MAD, while the green markers give the ratios provided by Huber et al. (2022) (their Table 1). Bottom right: ratio between P2P-MDV from 20-s (binned to 120-s) and 120-s cadence custom aperture data. Red markers again indicate median-binned values, with uncertainties given by the standardised MAD.
In the text
thumbnail Fig. 8

Comparison of noise levels and predicted detectability in ATL3 for stars with and without seismic detections. Left: correspondence between the global asteroseismic νmax parameters and the high-frequency PSD noise level for stars with positive seismic detections in blue, for stars identified as classical pulsators (e.g. δ Sct/γ dor), in red, and for stars without a seismic detection, in green. The νmax and colouring of the non-detection cases are given by the νmax and detection probability (Pdet) returned by the ATL3 (Hey et al. 2024), and except for a few cases, we only include stars with Pdet > 50%. The non-detection νmax values have been offset (horizontal line indicates the offset from the original position) by increasing νmax by 16%, corresponding to the apparent overall bias of the ATL3 values, as seen from Fig. 6. Stars with a noise level above 20 ppm2/μHz have been offset to this value (dotted line). The coloured lines give the median binned noise levels of stars with (blue) and without (green) detections against νmax. Right: correspondence between the maximum ATL3 Pdet (for either 20- or 120-s cadence) against νmax, with the same colouring and νmax as in the left panel. Stars with a Pdet below 50% have been offset to this value (dotted line). For the stars with seismic detection (blue), we indicate with small horizontal lines the ATL3 predicted νmax.
In the text
thumbnail Fig. 9

Aitoff sky projection in Galactic coordinates showing (circular markers) the TLS, with the marker size corresponding to the star’s visual magnitude (see legend key). The PLATO long-stare (LOP) fields are shown in blue (with shade corresponding to the number of 24, 18, 12, or 6 overlapping cameras). Yellow markers indicate targets identified as being within current PLATO LOP field definitions (see text), while orange markers indicate targets within 5° of the LOP field boundaries. Stars that are in binary systems and listed in Table D.5 are further marked with a cross (×). The TESS CVZs are given by the red circles that partly overlap the PLATO fields. The red line indicates the sky’s equatorial plane, while the green line gives the ecliptic plane.
In the text
thumbnail Fig. 10

Habitable Worlds Observatory target stars in terms of their luminosity and distance, with values adopted from the tables of Mamajek & Stapelfeldt (2024). Filled markers indicate stars with detected oscillations, with the colour and shape indicating the HWO tier (see legend). Stars with unfilled black thick-edged markers were considered for analysis, while black thin-edged ones did not conform to the selection criteria for our sample. Stars with red thick-edged markers were considered but did not have data available before S77, as considered in this analysis (but will later in Cycle 7), while red thin-edged ones are not scheduled for observations with TESS. The legend showing the different tiers gives, in parentheses, the number of detections out of the total number of stars in the tier, and lastly, the number of stars from each tier that we analysed.
In the text
thumbnail Fig. 11

TESS luminaries stars plotted in terms of visual magnitude against declination, with the colour indicating the expected angular diameter (see colour bar). Stars with a thick outline have a published interferometric angular diameter (Table D.4), and stars with a cross (×) are binaries listed in Table D.5.
In the text
thumbnail Fig. 12

Stars identified to match our solar-analogue criteria (see Sect. 5.8). The marker size indicates the visual magnitude (see legend), while the colour gives the [Fe/H]-value from Casagrande et al. (2011). Empty markers indicate known bright oscillators from ground-based surveys (and 16 Cyg B from Kepler) that match the solar-analogue criteria, but where no detection was obtained in this study. Markers with a thick black edge indicate the stars that are already known oscillators (see Table D.1). Left: identified (potential) solar analogues in terms of distance and νmax (α Cen A at a distance of ~1.35 pc is moved to a higher distance for a better display of the sample). The horizontal dotted line gives the solar νmax-value for reference. Right: identified (potential) solar analogues in terms of Teff and luminosity, using magnitudes and distances from the TESS Input Catalog (TICv8.2; Paegert et al. 2021). The dotted lines provide the solar values for reference. For reference, we also show 1 M⊙ MIST16 evolutionary tracks (Dotter 2016; Choi et al. 2016) with [Fe/H]-values of −0.3, 0, and +0.3 dex (increasing with tracks from left to right; also see color bar).
In the text
thumbnail Fig. B.1

Comparison between SPOC and K2P2 apertures. Left: Median (across sectors) aperture size in pixels against TESS magnitude for stars observed in 120-s cadence for apertures defined by SPOC and the adopted joint custom K2P2 + SPOC apertures (see legend). Middle: Number of pixels contributed to the custom apertures by SPOC (i.e., number of pixels not contained in the K2P2 aperture) as a function of TESS magnitude. Right: Difference in K2P2 and SPOC aperture size relative to the median, with points colour-coded according to TESS magnitude. The dashed horizontal line provides the median level across sectors, while vertical lines indicate shifts in pointing (see legend), typically coinciding with different sectors.
In the text
thumbnail Fig. B.2

Properties of TESS pixel stamps for 120-s (top row) and 20-s (bottom row) cadence observations. First column: Median pixel stamp width and height across sectors for individual stars as a function of their TESS magnitude. Second (third) column: Stamp width (height) as a function of sector, coloured by TESS magnitude. Width values have been dithered by up to ±0.5 pixels to show any dependence on magnitude better. Vertical lines indicate changes in pointings (see Fig. B.1, right panel). For the heights, red markers indicate stars that are found to have a pixel stamp height that is smaller than two or more fainter stars, i.e., breaking with the general monotonic increase in stamp height with decreasing TESS magnitude seen on the first column. Fourth column: Total stamp size as a function of sector, colour-coded according to TESS magnitude.
In the text
thumbnail Fig. C.1

Comparison between ATL3 input/output and measured values for the TLS and literature values for the K2 (Lund et al. 2016, 2024) and Kepler (Lund et al. 2017; Mathur et al. 2022; Serenelli et al. 2017) samples. Left: Relative difference (measured-predicted) between measured and predicted values of νmax from ATL3 with its standard priority of inputs on Teff and log g (primarily from Gaia DR3’s gspphot) against TESS magnitude. Middle (Right): Relative difference on Teff (log g) for the three samples. For the K2 and Kepler samples, the measured values are obtained from the literature and adopted from Casagrande et al. (2011) for the TLS. The black markers indicate median binned values of the combined samples.
In the text

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