Press Release
Open Access
Issue
A&A
Volume 670, February 2023
Article Number A136
Number of page(s) 28
Section Planets and planetary systems
DOI https://doi.org/10.1051/0004-6361/202245129
Published online 20 February 2023

© The Authors 2023

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.

This article is published in open access under the Subscribe to Open model. Subscribe to A&A to support open access publication.

1 Introduction

Over the next few years, new space- and ground-based observatories (e.g., the James Webb Space Telescope – JWST and the ESO Extremely Large Telescope – ELT) will be crucial to extending the current boundaries of planetary science. These instruments will boost the exploration of the atmospheres of exo-planets to an unprecedented level of precision, and probe the gas content of super-Earths and terrestrial exoplanets. It is thus of key importance to identify new low-mass exoplanets amenable to future atmospheric characterization.

A central feature of small exoplanets (R < 4 R) at small orbital separation (P < 100 days) is the radius valley (Owen & Wu 2013; Fulton et al. 2017; Cloutier & Menou 2020) separating solid super-Earths from gaseous sub-Neptunes. One possible explanation for this dichotomy is runaway evaporation or grinding of the atmospheres of low-mass exoplanets by a strong extreme ultraviolet (XUV) stellar radiation (Owen & Wu 2013). Alternative explanations are core-powered mass loss (Ginzburg et al. 2018), impact erosion by planetesimals (Shuvalov 2009), and formation of distinct rocky and non-rocky planet populations with delayed gas accretion (Lee et al. 2014; Lee & Connors 2021). Around solar-like stars, photoevaporation and core-powered mass loss scenarios predict that the valley location shifts toward a lower radius with lower stellar mass and larger orbital period (Van Eylen et al. 2018; Fulton & Petigura 2018; Martinez et al. 2019; Wu 2019; Gupta et al. 2022).

Around low-mass stars, Cloutier & Menou (2020) find that the slope of the valley around M-dwarfs in the radius-period diagram is inverse to the valley slope around FGK stars, leading to increasing radius of the transition with orbital period. This relationship is more compatible with a gas-depleted planet formation scenario in which solid cores increase in size with increasing distance to the star (Lee et al. 2014; Lopez & Rice 2018). Similarly to solar-like stars, for planets around low-mass stars, photoevaporation and core-powered mass loss are also expected to shape a radius gap with a negative slope in RpP diagram. However, this slope is expected to approach closer and closer to zero with decreasing stellar mass (Gupta et al. 2022).

Here we characterize a candidate planet with super-Earth-to-sub-Neptune size, that was detected and identified around the M2-type star TOI-1695, with the NASA Transiting Exoplanet Survey Satellite (TESS; Ricker et al. 2015). With an initially estimated radius of 1.8 R and an orbital period of 3.134 days, TOI-1695.01 lies in a radius-separation region where the question of formation and evolution of the atmosphere of super-Earth or sub-Neptune around a low-mass star can be tackled. Investigating whether or not such a key planet harbors a significant atmosphere may help explain which scenario is dominantly shaping the radius valley depending on the host star mass.

In this paper we seek to first establish the planetary nature of TOI-1695.01, and to characterize its mass and radius by combining TESS photometric measurements with radial velocity (RV) variations observed with the infrared (0.98-2.45 μm) SpectroPolarimetre InfraRouge (SPIRou; Donati et al. 2020) installed on the 3.6m Canada-France-Hawaii Telescope (CFHT).

In Sect. 2 we review the observations available for TOI-1695, including those from TESS and SPIRou. In Sect. 3 we characterize the host star using spectroscopy, spectrophotometry, and spectropolarimetry. In Sect. 4 we characterize the planet properties through a joint fit of TESS and SPIRou data. In Sect. 5 we discuss the results. We summarize and conclude in Sect. 6.

thumbnail Fig. 1

Full TESS light curve separated in sectors (gray dots) and binned with a 0.1-day timestep (black). The identified transit locations are shown as orange vertical lines.

2 Observations

2.1 TESS light curves

TOI-1695, also known as TIC-422756130.01 in the TESS Input Catalog (TIC), was covered by TESS sectors 18, 19, 24, 25, and 52 between November 2019 and June 2022 with 120 s cadence. In the data from these observing campaigns, a signal at a 3.13-day period is found using the box least squares (BLS) algorithm (Kovács et al. 2002) with 34 transits found, of which 32 were already identified in the data validation time series (hereafter DVT; Twicken et al. 2018; Li et al. 2019) of sectors 18-25, allowing the event TOI-1695.01 to be identified. We retrieved and used in the rest of this study the Science Processing Operations Center Pipeline (SPOC; Jenkins et al. 2016) simple aperture photometry with presearch data conditioning (PDCSAP; Smith et al. 2012; Stumpe et al. 2014). The full SPOC PDCSAP light curve with all identified transits is shown in Fig. 1.

We first performed a preliminary fit of the TESS PDCSAP data of TOI-1695 by a photometric batman model (Kreidberg 2015) with a third-degree polynomial modeling of the continuum of all individual transits. It has a transit depth of ~1000 ppm for a total transit duration of 1.2 h and a period of 3.134319±0.000026 days. It was pointed out1 that the SPOC photometry pipeline was prone to overestimating the background level and overcorrecting for background in crowded fields in the TESS primary mission (sectors 1−26). The pipeline was updated to prevent this in the extended mission (sectors 27+). We estimate that the combined transit search of the TOI 1695 data for sectors 18, 19, 24, 25, and 52 would have overestimated the transit depth (planet radius) by approximately 2.8% (1.4%). This is a minor correction compared to the uncertainties (~8.9%) resulting from the full analysis performed in Sect. 4.

Using TRICERATOPS (Giacalone & Dressing 2020), we examined possible sources of false-positive (FP) transit detection in the TOI-1695 light curve, from the system itself and from nearby background systems within the TESS aperture. The full list of possible causes of a FP is given in Table 1 of Giacalone et al. (2021). We show the aperture used in sector 18 and possible contaminating sources around TOI-1695 in Fig. A.1. This source is located in a crowded region with 411 neighbors with T-mag<18 at less than 200″. At most eight stars identified in the different TESS apertures could be contributing to the transit signal with TIC IDs 422756137, 422756132, 422756120, 629325853, 422756126, 422756145, 422756114, and 422756147. They have a flux ratio with TOI-1695 in the TESS passband between 5.3 and 7.4 mag. We find an FP probability of transit from TOI-1695 itself of 0.18% and an FP probability of background star pollution of 0.10%. These probabilities account for the contrast curve around TOI-1695 obtained in Sect. 2.3. According to the criteria defined by Giacalone et al. (2021), TOI-1695 b is thus a “validated” planet. Nevertheless, complementary follow-up with RV, as performed in the present paper, is necessary to independently validate its planetary nature and measure its mass.

Beyond the transit signal, a 15-day modulation with an average amplitude of ~175 ppm seems to be present in the PDCSAP light curves. A periodogram of the TESS PDCSAP data produced with lightkurve (Lightkurve Collaboration 2018) is shown in Fig. 2. This 15-day periodic signal is in fact mainly seen in Sector 18 with an amplitude of ~700 ppm (Fig. 1). We discard effects from variable background stars since, given the sources identified above within TOI-1695 apertures, such amplitude of variations in the light curve would require those sources to have proper oscillations >10%, which is rare (McQuillan et al. 2012). If not of instrumental origin, which we cannot fully exclude, this fluctuating signal could well be due to spots, in which case the 15-day period would be linked with the rotation period of the star. We discuss this possibility in more detail and compare it to other observations in Sect. 3.4.

thumbnail Fig. 2

Periodogram of the TESS SPOC light curve with a peak at ~15 days for a 175 ppm signal.

2.2 SPIRou spectra

2.2.1 Observations

SPIRou is a near-infrared high-resolution (980–2450 nm; R = 75,000) high-stability (~1 m s−1) velocimeter and spectropo-larimeter installed at the Cassegrain focus of the 3.6 m CFHT at Mauna Kea (Donati et al. 2020). TOI-1695 was observed between December 2020 and January 2022 with SPIRou as part of the large program SPIRou Legacy Survey (SLS; ID P42, PI: Jean-François Donati). It was observed at 45 observing epochs with four polarimetric exposures per epoch, totaling 180 SPIRou spectra. Table F.1 presents the observation log associated with each exposure. All SPIRou observations of TOI-1695 consist of polarization sequences, each split into four sub-exposures associated with different rhomb retarder configurations, from which we retrieve the corresponding Stokes I (unpolarized) and V (circularly polarized) spectra of the star at each epoch (Donati et al. 2020). The sequence number 1−4 of any sub-exposure is also given in Table F.1. Science data are acquired on fibers A and B fed by the two orthogonal states of the selected polarization, while fiber C is used to simultaneously record the spectrum of the SPIRou Fabry-Pérot RV reference, as described in Hobson et al. (2021).

A calibration sequence, including flats, darks, and spectra of comparison lamps (Fabry-Pérot, UNe), is performed every afternoon and morning, preceding and following each night of observation with SPIRou. A master-dark is constructed from a subset of well-characterized darks. The SPIRou detector software also archives ramp-files, which are detector images in ADU/s; during their creation a correction for nonlinearity is performed. Early-type stars (A-type) are observed nightly as telluric absorption standards. Bright inactive cool stars are also regularly observed as RV standards. These data are used to calibrate the measurements extracted from the SPIRou spectra. The detailed calibration sequence is described in Cook et al. (2022).

2.2.2 APERO reduction

The raw data were first reduced with the SPIRou reduction software, A PipelinE to Reduce Observations (APERO) with version 0.6.132 (Cook et al. 2022), hereafter v6. APERO first corrects detector effects, removes constant background thermal components, and identifies bad pixels and cosmic-ray impacts. It then calculates the position of 49 of the 50 echelle spectral orders2 and optimally extracts spectra from fibers A, B, and C into 2D order-separated e2ds and 1D order-merged s1d spectra. A blaze function is derived from the flat-field exposures.

The wavelengths are calibrated on the spectrum collected on Fiber C. An absolute calibration of wavelengths with respect to the Solar System barycentric rest frame using the current Barycentric Earth RV (BERV) and the Barycentric Julian Date (BJD) of each exposure is performed by the code barycorrpy (Kanodia & Wright 2018; Wright & Eastman 2014). Finally, for each exposure (e.fits file) APERO calculates the corresponding spectrum of the telluric transmission out of the whole collection of standard star observations carried out with SPIRou since 2018 using PCA algorithm (Artigau et al. 2014) and divides it out, producing a telluric-corrected t.fits spectrum. APERO also calculates the Stokes V spectra using the method of Donati et al. (1997), as described in detail in Martioli et al. (2020).

A more advanced version of the APERO pipeline, version 0.7.194, hereafter v7, was released during the writing of this work (Artigau et al. 2022; Cadieux et al. 2022). We used both v6 and v7 in our study, and compare them in Sect. 4.

2.2.3 RV derivation

The RV were derived from the telluric corrected t.fits spectra using the line-by-line (LBL) algorithm (Artigau et al. 2022). As in Bouchy et al. (2001), LBL requires a template spectrum with a S/N that is as high as possible since a derivative of the template is used to determine the RV of each spectral line. For TOI-1695, with only 40 epochs, the combined spectrum produced by APERO reaches a S/N that is much lower than other bright standard stars monitored with SPIRou. Instead of TOI-1695, we thus used as template another target with a similar M2V spectral type, GL15A (Teff = 3600 K; log g = 4.8 cgs units; [M/H] = −0.16 dex; Passegger et al. 2019; Cristofari et al. 2022b). Table 2 shows the stellar parameters that we find for TOI-1695. We combined 1040 spectra of GL15A with for each a S /N ~ 350 at 1670 nm leading to a template with S/N close to 10000.

The second and third derivatives of each spectral line are used as a proxy for the full width at half maximum (FWHM) and bisector span (BIS; Artigau et al. 2022). Their variations with respect to RV are studied in Sect. 3.3.

Calibration drifts are derived for all RV measurement epochs using the simultaneous Fabry-Pérot on fiber C. They lead to drift-corrected (DC) RVs. An average zero-point correction (ZPC) is also derived from the monitoring of RV standards (Cadieux et al. 2022). Our reference RVs in the rest of the paper will be those corrected from drift and zero-point, but we do a comparison of RV data reduction with or without DC and ZPC when analyzing the planet signal in Sect. 4.

All RV data, FWHM and BIS used for TOI-1695 and reduced with versions v6 and v7 are available in Tables G.1 and G.2, respectively. Their variations are inspected in greater detail in Sect. 3.3. In both cases we ignored the measurement of epoch 44 (JD-2459605), which only has a single sub-exposure. With v6, we also ignored the measurements of epoch 33 (JD-2459591), for which the drift calculation failed.

2.3 Imaging

As part of a standard process to validate transiting exoplanets and to assess the possible contamination of bound or unbound companions on the derived planetary radii (Ciardi et al. 2015), TOI-1695 was observed with infrared high-resolution adaptive optics (AO) imaging at Keck Observatory with the NIRC2 instrument on Keck-II behind the natural guide star AO system (Wizinowich et al. 2000). The observations were made on 2020 May 28 UT in the standard three-point dither pattern that is used with NIRC2 to avoid the lower left quadrant of the detector, which is typically noisier than the other three quadrants. The dither pattern step size was 3″ and was repeated twice, with each dither offset from the previous dither by 0.5″. The camera was in the narrow-angle mode with a full field of view of ~10″ and a pixel scale of approximately 0.01″ per pixel. The observations were made in the narrowband Br-γ filter (λ0 = 2.17 μm; Δλ = 0.03 μm with an integration time of 4 s with one coadd per frame for a total of 36 s on target.

The AO data were processed and analyzed with a custom set of IDL tools. The science frames were flat-fielded and sky-subtracted. The flat fields were generated from a median of dark subtracted flats taken on-sky, and the flats were normalized such that the median value of the flats is unity. Sky frames were generated from the median average of the nine dithered science frames; each science image was then sky-subtracted and flat-fielded. The reduced science frames were combined into a single combined image using an intra-pixel interpolation that conserves flux, shifts the individual dithered frames by the appropriate fractional pixels, and median-coadds the frames. The final resolution of the combined dithers was determined from the FWHM of the point spread function to 0.054″.

The sensitivities of the final combined AO image were determined by injecting simulated sources azimuthally around the primary target every 20° at separations of integer multiples of the central source’s FWHM (Furlan et al. 2017). The brightness of each injected source was scaled until standard aperture photometry detected it with 5σ significance. The resulting brightness of the injected sources relative to the target set the contrast limits at that injection location. The final limit at each separation was determined from the average of all of the determined limits at that separation and the uncertainty on the limit was set by the RMS dispersion of the azimuthal slices at a given radial distance.

No additional companions to within the limits of the data were detected (see Fig. 3). With contrast sensitivities of ~3.5 mag at 0.06″ (2.7 au) and ~7 mag at 0.5″ (22 au), the near-infrared (NIR) AO observations indicate that there are likely no stellar companions down to ~M6 – L9 (see E. Mamajek’s compilation of Mean Dwarf Stellar Colors version 2021.03.023).

thumbnail Fig. 3

Companion sensitivity for the Keck NIR adaptive optics imaging. The black points represent the 5σ limits and are separated in steps of 1 FWHM (~0.054″); purple represents the azimuthal dispersion (1σ) of the contrast determinations (see text). The inset image is of the primary target showing no additional companions to within 3″ of the target.

3 Stellar characterization

3.1 Stellar parameters

We used four different methods to measure the stellar parameters of the star, including the effective temperature Teff, the surface gravity log g, the metallicity [M/H], the micro-turbulent vmicro, the macro-turbulent velocity vmacro, the bolometric flux Fbol, the bolometric luminosity ℒbol, the stellar mass M*, and the stellar radius R*. The methods of spectral energy distribution (SED) and of the TESS Exoplanet Follow-up Observing Program (exo-FOP4 are summarized in Sect. 3.1.1). The methods of comparison to synthetic spectra, based on SPIRou spectra, are explained in Sect. 3.1.2. All results are summarized and compared in Table 2.

thumbnail Fig. 4

Spectral energy distribution of TOI-1695. Red symbols represent the observed photometric measurements, where the horizontal bars represent the effective width of the passband. Blue symbols are the model fluxes from the best-fit NextGen atmosphere model (black curve). The green curve is the published Gaia DR3 spectrum for this star.

3.1.1 Spectral energy distribution

We performed an analysis of the broadband SED of the star together with the Gaia EDR3 parallax (with no systematic offset applied; see in particular Stassun & Torres 2021), in order to determine an empirical measurement of the stellar radius, following the procedures described in Stassun & Torres (2016) and Stassun et al. (2017, 2018). We retrieved the JHKS magnitudes from 2MASS, the W1-W4 magnitudes from WISE, and the GBPGRP magnitudes from Gaia, as well as the near-ultraviolet (NUV) flux from GALEX. Together, the available photometry spans the stellar SED over the wavelength range 0.2-22 μm (see Fig. 4). All photometric data are summarized in Table 1.

We performed a fit using NextGen stellar atmosphere models (Hauschildt et al. 1999), with the free parameters being effective temperature (Teff) and metallicity ([Fe/H]), as well as extinction AV, which we limited to maximum line-of-sight value from the Galactic dust maps of Schlegel et al. (1998). The resulting fit (Fig. 4) has a best-fit AV = 0.02 ± 0.02, Teff = 3630 ± 50 K, and [Fe/H] = 0.0 ± 0.5, with a reduced χ2 of 1.3. It also fits well with the calibrated mean Gaia BP/RP magnitudes spectrum available for this star in Gaia DR3 (Montegriffo et al. 2023) as shown in Fig. 4. Integrating the (unreddened) model SED gives the bolometric flux at Earth, Fbol = 6.82 ± 0.24 × 10−10 erg s−1 cm−2, which with the Gaia parallax gives directly the bolometric luminosity, Lbol = 0.0431 ± 0.0015 L. Taking the Fbol and Teff together with the Gaia parallax gives the stellar radius, R* = 0.525 ± 0.017 R. In addition, we can estimate the stellar mass from the empirical MK relations of Mann et al. (2019), giving M* = 0.539 ± 0.027 M. These mass and radius values lead to log g = 4.72±0.14 in cgs units.

The stellar parameters from the TESS project can be found in the TESS-exoFOP and are also reported in Table 2. TOI-1695 belongs to the specially curated list of cool dwarfs of the TIC (Muirhead et al. 2018; Stassun et al. 2019). As explained in Stassun et al. (2019), the effective temperature Teff is retrieved from the cool dwarfs catalog of TIC v7 (Stassun et al. 2018). The radius is derived from the Teff, Gaia magnitudes, and Gaia parallax using Eq. (4) of Stassun et al. (2019). The mass is obtained from the spline-interpolation of an empirical mass-Teff relationship.

The parameters derived from the two methods are consistent within the uncertainties.

Table 1

Main astrometric and photometric data of TOI-1695.

3.1.2 Comparison to synthetic spectra

We also derive the stellar parameters for TOI-1695 from a highresolution template spectrum built from the tens of individual spectra acquired with SPIRou. The method used consists in the comparison of a grid of synthetic spectra to the template spectrum. We use two independent methods, both based on MARCS models (Gustafsson et al. 2008).

The first method is based on a process briefly described below (more details in Cristofari et al. 2022b,a). This process was tested and calibrated on reference stars. The synthetic spectra used for this analysis were computed from the MARCS model atmospheres with the Turbospectrum radiative transfer code (Plez 2012), for a wide range of effective temperatures (Teff), surface gravities (log g), and metallicities ([M/H]). The models and template spectrum are compared by computing a χ2 value in key regions, containing lines that are both well reproduced by the models and sensitive to the stellar parameters. We therefore retrieve a three-dimensional grid of χ2 values, and perform a paraboloid fit to identify the best-fitting parameters and estimate error bars on these values. For TOI-1695, the parameters estimated with this method are Teff = 3627 ± 31 K, log g = 4.60 ± 0.05 dex, and [M/H] = 0.10 ± 0.10 dex. A fit of the star’s projected rotational velocity v sin i, fixing the micro-turbulent velocity to vmicro = 1 km s−1, the macroturbulence velocity to vmacro = 0 km s−1 , and the resolution linewidth of SPIRou to FWHM ~ 4.3 km s−1, leads to a v sin i = 1.9±0.2 km s−1. Given that vmacro is fixed to 0, we can only interpret this measurement as an upper limit on v sin i; therefore, <2.5 km s−1 at 1σ, which is at most on the order of the pixel scale of the SPIRou detector (~2.3 km s−1; Donati et al. 2020). Modeled and observed spectral lines are compared together in Fig. B.1.

The second method used is described in greater detail in Sect. 4.2 in Martioli et al. (2022). We calculated a grid of 650 000 synthetic spectra generated with MOOG (Sneden et al. 2012) covering Teff from 2900 to 4100 K with 50 K step, log g from 3.5 to 5.3 with 0.1 dex step, [M/H] from −0.6 to 0.5 with 0.11 dex step, alpha from −0.2 to 0.4 with 0.06 dex step, and vmic from 0.5 to 4 km s−1 with 0.35 km s−1 step. We modified the Apache Point Observatory Galactic Evolution Experiment (APOGEE) line list (Smith et al. 2021) by selecting only the lines that are most sensitive to Teff and log g within the spectral range from 1.5 to 1.7 μm. We searched the grid for the best-fitting parameters, which are adopted as priors in the iSpec integrator (Blanco-Cuaresma 2019) to calculate the synthetic spectrum that best fits our SPIRou data within the domain of the selected lines. We used the MOOG code with the MARCS GES model (Gustafsson et al. 2008) and Asplund et al. (2009) solar abundances. In total, 78 spectra were compared. The parameters of TOI-1695 estimated with this method are Teff = 3711 ± 47 K, log g = 4.53 ± 0.13 dex, [M/H] = 0.00 ± 0.08 dex, and vmicro = 0.3 ± 0.7 km s−1.

The values derived with these two methods agree well with each other; moreover, there is good agreement with the SED fitting at 2σ. In the rest of the analysis we thus adopt the values Teff = 3650±40 K, log g = 4.72±0.14, and [Fe/H] = 0.0±0.1 dex. We also adopt M* = 0.54±0.03 M and R* = 0.53±0.02 R derived from the SED fitting (Sect. 3.1.1 above) that rely directly on the spectrophotometry of the star with robust empirical relations.

3.2 Polarimetry

An independent polarimetric reduction and least squares decomposition (LSD) analysis of TOI-1695 SPIRou data using the Libre-Esprit (LE) pipeline (Donati et al. 1997, 2020), leads to the polarimetric longitudinal field B for TOI-1695 shown in Fig. C.1 and given in Table G.3. The B measurements are consistent with those obtained in the classical APERO reduction (e.g., Martioli et al. 2022). At a given epoch, B is directly determined from the Stokes V and Stokes I profiles (Donati et al. 1997). We find that the star has a weak polarimetric variability and magnetic activity. The Stokes V profiles have a for a constant model, while the null polarization N (Donati et al. 1997) has a . Any variability in the data is thus fully consistent with the error bars.

A power spectrum of the B data (Fig. 5) shows peaks beyond 20 days, all with a false-alarm probability (FAP) >10%. Thus, no peculiar polarimetric signal is significantly detected for TOI-1695 given the current precision. The period of ~48 days nevertheless seems to dominate the power-spectrum at low frequencies. We fitted the data with a quasi-periodic Gaussian process (GP; see, e.g., Haywood et al. 2014; Aigrain et al. 2015) using the same codes as in Donati et al. (2017), at an initial period of 48 days with typical decay time τdecay = 500 days and smoothing factor γsmooth = 0.6. We obtain an almost flat GP model with a marginal likelihood difference Δ In ℒ = 2 compared to a constant model. The GP model is plotted over the longitudinal field in Fig. C.1.

We can thus conclude that (i) we do not see trace of a 15-day signal, as reported in Sect. 2.1; (ii) the star was, if active, passing through a quiet phase during SPIRou observations; and (iii) more data are needed to confirm whether the hinted 48-day period could be a trace of the star’s rotation signal.

Table 2

Spectral parameters of the star.

thumbnail Fig. 5

Periodogram of the longitudinal magnetic field variations, with dominant frequencies highlighted in blue. The window function is shown in orange, with a peak close to the synodic orbital period of the Moon.

3.3 RV variations and stellar activity effects

We searched for correlations between the RVs and the activity indicators, bisector span (BIS), and FWHM variations obtained from the two APERO version v6 and v7 studied in this work. The Pearson correlation coefficient for RV-BIS are Rv6 = 0.14 (p-value = 0.07) and Rv7 = −0.22 (p-value = 0.004). The Pearson correlation coefficient for RV−FWHM is Rv6 = −0.13 (p-value = 0.10) and Rv7 = 0.06 (p-value = 0.46). The only significant slope that we detect (1.7σ) is for the FWHM and RV derived from v6 of −0.62+0.37 m s−1 per m s−1. It disappears with v7 suggesting that some instrumental signal was corrected from the SPIRou data with the last implementations in the pipeline.

We thus find no significant correlations of astrophysical origin between RV and the activity indicators FWHM and BIS. This is compatible with the weak magnetic activity deduced from the almost flat longitudinal field B.

The periodograms of the BIS and FWHM compared to the periodogram of the RVs in Fig. 6 show no significant peaks with FAP < 5% at periods <20 days, especially none at around 3.13 days, the orbital period of the candidate planet detected with TESS. Peaks with FAP < 1% appear at periods beyond 20 days, some of which coincide with large peaks of the window function.

On the other hand, in the RV periodogram we have a positive detection of the candidate planet signal at 3.13 days with a FAP ~ 1%. Two other strong peaks on both sides of this signal are due to aliases with the observing window frequencies. Most of the structures in the near periods of three days significantly weakens after fitting out a 3.13-day Keplerian to the RVs. As seen in the RV residuals periodogram in Fig. 7, residual RV variations then mainly concentrate around ~ 16 days. Interestingly, it is close to the 15-day period spotted in the light curve continuum variations of TESS sector 18 (Sect. 2.1). This would suggest an origin intrinsic to TOI-1695 itself or linked to its direct environment, even if no corresponding signal is detected in polarimetry. However, studies of the TOI-1695 surroundings in Sect. 2.3 exclude that the RV variations could originate from background stars at less than 3″ distance.

Inspecting the periodogram of the LBL RV derived using the APERO v7 (Fig. D.1) leads to similar conclusions. Only the power at low frequencies is significantly reduced from the RV, FWHM and the BIS variation, leaving only insignificant peaks, some of which are most likely bias of the Earth’s orbital period with the ~20-day gap period. There is indeed some power at long periods of ~365 days, which could be linked to yearly changes in the instrument resolution.

thumbnail Fig. 6

Lomb-Scargle periodograms with FAP levels 0.01 and 0.05 indicated as dotted and dashed lines for data extracted with the APERO v6 version: (from top to bottom) RV, FWHM, and BIS. They are compared to the window function (red solid line). The vertical orange solid line indicates the 3.134-day period of the TESS transit signal. The moon synodic orbital period (29.53 days) is also shown as a green solid line.

thumbnail Fig. 7

Periodogram of the residuals O-C after fitting a 3.134-day Keplerian to the RVs. Same color-coding as in Fig. 6.

3.4 Concluding remark on the rotation period of TOI-1695

The TESS photometry and SPIRou RVs show traces of signals with periods within 14−19 days. A rotation period of the star with a period in this range would be in agreement with the M2V spectral type found for TOI-1695 (see, e.g., Goulding et al. 2012; Goulding 2013; Basri et al. 2011; Moutou et al. 2017). There are few other observations that agree or not with this stellar rotation period.

First, photometry and RV do not strictly agree with each other on the period of modulation. Photometry shows a periodogram peak at 14.8 days. The LBL RV calculated from the APERO v6 data instead show a peak at 16 days. If they are calculated from APERO v7, the peak shifts to ~18 days.

Second, if the rotation period of the star were equal to 15 days, following the hinted photometric variations in Sect. 2.1, the stellar radius of 0.53 R would imply a ν sin i equal to 1.8 km s−1, or less if the stellar spin and planet orbit are misaligned. This could agree with the ν sin i of 1.9+0.2 km s−1 that was obtained in Sect. 3, implying in this case that the equatorial plane of the star is aligned with the orbital plane of TOI-1695 b.

Third, the SPIRou polarimetry of the star does not detect a 15-day periodicity of the star’s magnetic field during the times-pan of the SPIRou observations. If due to stellar rotation, we would have seen a stronger field and detected the rotational modulation in polarimetry.

Finally, we retrieved the available public photometry data to search for periodic signals indicative of stellar rotation. As of 10 September 2022, TOI-1695 was observed by the All Sky-Automated Survey for SuperNovae (ASAS-SN Shappee et al. 2014; Kochanek et al. 2017) at 2043 epochs. ASAS-SN images were obtained through a Sloan g filter (and previously, for 879 epochs, in V-band). A Lomb-Scargle periodogram analysis shows a signal at 52.4 days in V-band, but no significant signal in g-band. We also retrieved 328 and 490 epochs of photometry in Sloan g- and r-band from the Zwicky Transient Factory archive (Bellm et al. 2019; Masci et al. 2019). We find a FAP < 1% signal at 27 days in the r-band data, but not in the g-band data. In principle, the 27-day signal could be an upper harmonic of the ≈52-day signal obtained from ASAS-SN, but there is no hint of a 15-day modulation. The absence of confirmatory detections in the other bands does not allow us to draw a firm conclusion about the rotation period of TOI-1695 from these data.

We conclude that the 15-day signal seen in TESS sector 18, as well as the 16-day (or 18-day) signal seen in the SPIRou LBL RVs, are likely not related to the rotation of the star. Polarimetry, spectroscopy, photometry, and RV agree nevertheless on a star rotation period greater than 15 days. Moreover polarimetry, V-band, and r-band photometry suggest a rotation period in the range 20–52 days. Such a long rotation period is found for M dwarfs with a stellar age >1 Gyr (Engle & Guinan 2018). We thus infer that TOI-1695 is more than 1 Gyr old.

4 Joint analysis of TESS and SPIRou data

Here we use the same recipe as Martioli et al. (2022) to fit the combined photometric and velocimetric data from respectively TESS and SPIRou recorded for TOI-1695. We run a Markov chain Monte Carlo (MCMC) algorithm with the emcee routine (Foreman-Mackey et al. 2013) with prior distribution shown in Table 3. We fix the quadratic limb darkening parameters u0 and u1 with respect to Claret (2017) and Claret & Southworth (2022), for a Teff = 3600 K. We initialize 32 walkers, sample parameter space on 20 000 steps, and burn the 10 000 first samples. The posterior distributions of parameters are listed in Table 4.

We apply the fit on different analysis schemes of the LBL RV, using both v6 and v7 reductions (hereafter RVV6 and RVV7). This allows us to compare the quality of the RV series extracted from both APERO versions and to qualify the improvements brought by the new pipeline. For both versions, fits without drift correction nor zero-point correction were poor in terms of χ2 and the O-C residuals. So we performed fits assuming the following:

  • DC+ZPC: accounting for the long-term calibration drift of the RV and a zero-point correction (see Sect. 2 for details);

  • DC+ZPC+GP: adding a quasi-periodic (QP) Gaussian process (GP) to the fit, as explained in more detail below.

The result of the joint Keplerian fit of both light curve and RVs are all compared in Table 4, assuming a circular orbit.

In the DC+ZPC scheme, the 3.134-day RV signal is found with a S/N higher than 5σ. This allows us to confirm the detection of the planet signal with a corresponding companion mass in the super-Earth-to-sub-Neptune regime ~6 M. Although compatible at 0.7σ, the semi-amplitude of the Keplerian model fitted to the RVv7 (3.6±0.7 m s−1) is smaller than that of the RVv6 (4.1±0.7 m s−1). On the other hand, the RV residual dispersion is slightly smaller for RVv6 than for RVv7 with respective reduced χ2 of 0.8 and 1.1, and absolute O-C dispersion of 6 m s−1 and 7 m s−1. We also noted a difference of ~460 m s−1 in systemic RV, which we attributed to a different treatment of the calibration between the two versions. The difference in the Keplerian solutions and residuals, implying systematic variations unaccounted for in both, motivated us to dig further with the GP analysis of those datasets.

The quasi-periodic Gaussian process that we used combines a squared exponential (SE) and a periodic kernel with the code george (Ambikasaran et al. 2015) to remove the periodic pattern in the RV and the TESS-LC identified in Sects. 2.1 and 3.3. The GP is trained separately on the residual RVs and on the photometric time series excluding the transits. The priors used on the GP parameters are added to Table 3. We were careful not to allow the decay time of the SE kernel to be smaller than twice the orbital period of TOI-1695 b, thus preventing the GP from overfitting and absorbing some of the orbital signal present in the RV, along with the recommendations given in Angus et al. (2018). For optimizing the GP hyper-parameters more efficiently using an MCMC, the photometric light curves were rebinned to time steps of 0.25 days. Figures 8 and 9 show the GP modeling of the TESS light curve and the SPIRou RV.

The QP GP fit of the light curve converged to a period of days. For the RVv6 it converged to a period of days and for the RVv7 to days. After applying and then removing the GP from the RV and photometric time series, we fit again the orbital and transit signals from the RV and LC. The DC+ZPC+GP analysis leads to the smallest RV residual dispersion with reduced χ2 of 0.6 and 0.5 respectively of RVv6 and RVv7. The flux dispersion ~2008 ppm is constant over all schemes, which is not surprising given that the light curve continuum is always detrended around the transits epochs. The Bayesian information criterion (BIC) among all schemes is clearly in favor of the DC+ZPC+GP scheme with the smallest BIC. The posterior distributions although different are compatible with the DC+ZPC solutions. As expected, the model semi-amplitudes of RVv6 (3.95±0.66 m s−1) and RVv7 (3.63±0.68 m s−1) are smaller (only slightly for RVv7) than without modeling the systematic variations. The Keplerian fit of the RVv7-GP residuals leads to the best residual dispersion among all analyzed datasets, σO−C = 4.8 m s−1, but with similar planetary parameters than without fitting the GP.

As an additional check, we ran a fit of the data in the DC+ZPC scheme, but considering a nonzero eccentricity. The eccentricity obtained when letting it freely vary is consistent with 0 at 1.3σ with e < 0.2. More importantly, at maximum likelihood, the BIC is larger than for the zero-eccentricity case. This validates that fixing the eccentricity to zero in the other fits is the correct, most parsimonious approach, consistent with a tidally circularized orbit of the planet, and moreover does not generate significant systematic errors on the other orbital parameters.

Varying the limb darkening coefficients led to wide posterior distributions of u0 and u1 with unrealistic values u0 ~ 0.5±0.4 and u1 ~ 0.9±0.7. Although compatible within the errors with the common values assumed in the rest of the analysis, they lead to less accurate planet radius estimations with highly biased posteriors on the semimajor axis and radius. Notably, the median of the radius posterior distribution is 1.77±0.21 R, but the maximum a posteriori point estimate is ~1.99 R. This shows that the present dataset is not able on its own to refine the limb darkening coefficients. It is more reasonable to rely on the known values of the limb darkening coefficients of an M 2 star.

After comparing all the different reduction and analysis schemes, we retained the solution with the smallest BIC. Finally, it is the DC+ZPC+GP derived from the APERO v7 that leads to the best solution. It can be trusted that the GP did not overfit the RV signal as the planetary parameters are similar when not fitting a GP. The corner plot of the posterior distribution of all varied and derived Keplerian parameters is shown in Fig. E.1. The best-fitting model of the transit curve is shown in Fig. 10, and the best-fitting models of the phase-folded LBL RV is shown in Fig. 11.

It results in a companion on an orbit of 3.1343 days with a planetary radius of 2.03±0.18 R and a planet mass of 5.5±1.0 M. This implies a sub-Earth planet density of 3.6±1.1 g cm−3, and an equilibrium temperature of 590±90 K.

Table 3

Prior distribution of fitted or fixed parameters entering the MCMC sampling.

Table 4

MCMC results of the photometry (TESS) and spectroscopy (SPIRou) joint fit.

5 Discussion of TOI-1695 b mass and radius

We find that TOI-1695 b is a sub-Neptune planet with an average density ~3.6 g cm−3 smaller than the Earth density. The mass and radius of TOI-1695 b are compared to other super-Earths and sub-Neptunes in Fig. 12, as obtained thanks to the pyexorama package (Zeng et al. 2021; Francesco et al. 2022). TOI-1695 b lies between two similar sub-Neptunes, Wolf-503 b (Peterson et al. 2018; Polanski et al. 2021) and TOI-270 d (Van Eylen et al. 2021; Günther et al. 2019) in terms of mass, radius, and temperature, at the border of the radius valley, but on the sub-Neptune side of it. It also has similar mass and radius to K2-3 b (Kosiarek et al. 2019), although warmer (463 K for K2-3 b).

5.1 Possible composition of the planet

We assume that a pure silicate core leads to a planet with a significant gas envelope in the formalism developed by Zeng et al. (2021). An M-R diagram comparing TOI-1695 b to other super-Earths and sub-Neptunes is shown in Fig. 12. Using the smint code (Piaulet et al. 2021) based on the formalism of Lopez & Fortney (2014), Zeng et al. (2016) and Aguichine et al. (2021), we considered three different scenarios:

  • a planet with H-He envelope and a solid interior leads to ;

  • a 100% silicate interior with pure water on top of it, leads to a water envelope with a mass fraction of ;

  • a water-world with unfixed iron content in the core of the planet leads to an iron-to-silicate ratio and a water envelope with a mass fraction of .

All of these scenarios agree on the evidence that, as for other sub-Neptune exoplanets of similar radius and mass, in particular for K2-3 b, a fraction of the planet mass should be in the form of a gaseous envelope. With Teq = 590±90 K, TOI-1695 b has an equilibrium temperature just below the water critical point, theoretically allowing water to be liquid if a surface pressure of about 200 atm can be sustained. However, if a significant atmospheric layer is present, the temperature will also certainly increase with pressure. Thus, water cannot be in a liquid phase on the surface of this planet, as was shown for GJ 3470 b, whose irradiation temperature is similar (Piette & Madhusudhan 2020).

thumbnail Fig. 8

Gaussian process fit of the light curve with a quasi-periodic kernel. Gray points are raw PDCSAP flux TESS data, and black points are the binned data with Δtbin = 0.25 days.

thumbnail Fig. 9

Gaussian process fit of the SPIRou RV in the case of the APERO v6 reduction (top) and the v7 reduction (bottom) with a quasi-periodic kernel.

5.2 Atmospheric mass loss vs. gas-depleted formation

The worlds with radius ~2 R are reputed to be underabundant around solar-type stars due to atmospheric loss forming Rp < 1.7 R planets (Kite & Schaefer 2021; Rogers & Owen 2021). However, TOI-1695 b, and a few others, exhibit such characteristics. Because it is located around an M dwarf, TOI-1695 b has undergone different irradiation conditions than planets around solar-type stars and has followed a different evolution with respect to photoevaporation.

Using Eq. (15) from Lecavelier Des Etangs (2007), we measure an order of magnitude for the evaporation rate for TOI-1695 b of dm/dt~1010 g s−1. This evaporation rate indicates that if a Η-He envelope is still present today in the atmosphere of this planet (less than 1% of the planet mass), it has to be, in this scenario, the remains of the evaporation of a much more massive envelope. If the gas represented about 10% of the mass of the initial planet, this primitive atmosphere would have had a lifetime shorter than 1.5 Gyr. This is about the minimum age of the system that we inferred from spectroscopic, photometric, and activity analysis of TOI-1695 in Sect. 3.

A lifetime of 10 Gyr is obtained for an envelope with 70% of the mass of the core, almost doubling the initial planet mass. Although possible, in this case the high mass loss rate requires much fine tuning of the envelope mass and the age of the planet to explain our observations. It would make this detection fortuitous, and thus unlikely if photoevaporation is responsible for the actual values of the mass and radius of TOI-1695 b.

Therefore, if the age of this system is closer to ~1 Gyr, then photoevaporation could be at the origin of a shallow Η−He atmosphere; but if the age of TOI-1695 is instead beyond 5 Gyr, then this planet was more likely formed as is with a small atmosphere, and survived because it formed far away and migrated inward. In this case the scenario proposed by gas-depleted formation (Lee et al. 2014) is preferred.

This is illustrated in Fig. 13. It compares TOI-1695 b to other similar exoplanets around cool stars (Teff < 4000 K) in an Rp-Porb diagram and compares it to rocky and non-rocky empirical transitions consistent with gas-depleted formation (Cloutier & Menou 2020) and thermally driven atmospheric escape (Martinez et al. 2019). This includes photoevaporation (Lopez & Rice 2018) as well as core-powered mass loss (Gupta et al. 2022) mechanisms, each predicting a similar negative slope of the radius valley in Rp-Porb space for low-mass stars. In contrast, the gas-depleted formation scenario can produce larger rocky super-Earths at longer orbital periods, resulting in a positive slope of the rocky-to-non-rocky transition. Exoplanets in the shaded region of Fig. 13 are interesting candidates to test these formation and evolution scenarios.

Determining the rocky or gaseous nature of exoplanets in the region enclosed by these two empirical Rp-Porb relations is therefore of key importance to determining which scenario dominates over the other around M dwarfs. Here, TOI-1695 b as non-rocky tends to favor the gas-depleted formation scenario. However, it is located close to the thermally driven mass loss limit, and given the age uncertainty on the host star, the photoevaporation or core-powered mass loss scenarios cannot be rejected.

thumbnail Fig. 10

Results from fits to the TESS light curve, with all the observed transits stacked, with the model taken from the maximum a posteriori point estimation, based on maximizing the posterior probability distributions for each parameter. At this point, T0 = 1791.5206 BTJD, Ρ = 3.1342799 days, a = 21.1 R*, Rp = 0.034 R*, and Ip = 89.4°.

thumbnail Fig. 11

Phase-folded model of the RV variation seen with SPIRou of the star TOI-1695 with a 3.13-day circular orbit. The data used here are derived from APERO v7. Gray dots gather all the RV data and black dots are binned with a time step of 0.05 days.

thumbnail Fig. 12

Mass-radius plot obtained based on the pyExoRaMa code (Zeng et al. 2021; Francesco et al. 2022). The red, green, and blue solid lines represent the mass-radius relation for respectively pure Fe, silicate, and H2O core. The yellow-brown z-contours (see Zeng et al. 2021 for details) with black solid lines represent the radius inflation due to gas in an envelope surrounding the planet core corresponding to pure silicate. The comparison planets (black circles) are taken from the TepCat database (Southworth 2011), selecting only exoplanets around M and Κ host stars. TOI-1695 b is shown as a blue star.

thumbnail Fig. 13

Rp-Porb diagram for exoplanets around cool stars (Teff < 4000 K) taken from the NASA exoplanet archive. The solid and dashed lines represent respectively the Cloutier & Menou (2020) and Martinez et al. (2019) empirical radius valley (see text). The shaded region shows exoplanets that from their gas content could be able to test gas-depleted formation and thermally driven mass loss scenarios. TOI-1695 b is highlighted as a thick-lined black circle.

5.3 Prospect for atmospheric observations

With a J magnitude of 9.6 for the star TOI-1695, the transits of TOI-1695 b can be observed in a search for atmospheric signatures. To characterize the potential for the detection of atmospheric absorption lines, we can evaluate the ratio of the atmospheric absorption depth to the noise in the transit light curve. The noise level depends on the wavelength, telescope, instrument, for example, and the depth of the atmospheric absorption depends on the species, abundances, oscillator strength, and number of the searched lines. Therefore, now only a relative S/N of detection can be calculated for an exoplanet atmosphere to be compared with other planets to be observed for the search of the same signature with the same instrument.

Here, we made the calculation in the J-band, which is currently available from space- and ground-based facilities, and which is close to the H2O band observed in a large number of exoplanets (e.g., Fraine et al. 2014; Benneke et al. 2019; Tsiaras et al. 2019; Mikal-Evans et al. 2021). The atmospheric absorption depth, which is the fraction of the stellar flux that is absorbed by the atmosphere during the transit, is proportional to the area of the absorbing layer, which is given by the scale height of the atmosphere (H) multiplied by 2πRp, where Rp is the planet radius, and inversely proportional to the stellar disk area (). The noise is assumed to be proportional to the square root of the stellar flux given by , where mJ is the star J magnitude. The atmospheric scale height is given by kT/μg, where T is the atmosphere temperature, k the Boltzmann constant, μ the mean molar molecular mass, and g is the planet gravity. Finally, with , where Mp is the mass of the planet, the signal-to-noise ratio is given by , in agreement with the transmission spectroscopic metric (TSM) as defined by Kempton et al. (2018, see also Cointepas et al. 2021).

We calculated the S/N expected for all known exoplanets transiting an M star, and normalized them to the value of 100 for AU Mic b, as done in Martioli et al. (2022). We used the catalog of exoplanets published in the Exoplanets Encyclopedia on 18 July 2022 (Schneider et al. 2011). For the J magnitudes, we used the tabulated values when available or calculated theoretical values from the V magnitudes and the stars’ effective temperatures assuming a blackbody spectrum. We considered only the M-type stars in the catalog; a star is considered to be an M-type if it is tabulated with this stellar type or if no stellar type is given in the catalog and its effective temperature is between 2200 and 3900 K.

The result is shown in Fig. 14 where we plotted the S/N of the atmospheric signatures and the TSM in the J-band as a function of the planetary mass for known exoplanets orbiting M-type stars with masses between 2 and 10 M. With an S/N of 4 of TOI-1695 b, in the 2–10 Earth mass range, several exoplanets yield a much better S/N. Even if we calculate the same plot for the S/N in the V-band, with a stellar type M2V and a V magnitude of 13.0, the situation is barely improved and the conclusion remains the same. Nevertheless, TOI-1695 b has a TSM of 48, a decent S/N to be reached in only 10 h of observation with JWST.

We also determined the emission signature metric (ESM; Eq. (4) in Kempton et al. 2018) of TOI-1695 b in the K-band, assuming a day-side temperature of the planet of 1.1×Teq. We found an ESM of 3, implying an S/N = 3 for a JWST detection of the planet in the K-band during a secondary eclipse. This is again significantly below the threshold for defining the best emission targets, but still challenging in term of detectability.

Thus, even though TOI-1695 b is not the best target in terms of precision, its atmosphere could still be characterized with a limited observation time on large telescopes such as JWST.

thumbnail Fig. 14

Signal-to-noise ratio (left axis) of the atmospheric signatures and TSM (right axis) in the J-band as a function of the planetary mass for exoplanets orbiting M-type stars with masses between 2 and 10 Earth mass. The S/N are normalized to a reference S/N of 100 for AU Mic b, as in Martioli et al. (2022).

6 Summary and conclusion

Thanks to RV follow-up observations with SPIRou at CFHT, we have established the planetary nature of a companion discovered by TESS around the star TOI-1695 with an orbital period of 3.134 days.

We showed that TOI-1695 b is a sub-Neptune planet, with a mass of 5.5±1.0 M and a radius of 2.03±0.18 R. We found hints of a supplementary weak variability in both photometry (P~12–15 days) and RV (P~16–19 days), but not present in activity indicators (polarimetry, FWHM, bisector span). These activity indicators moreover do not show signs of any clear modulation below a period of ~20days. It implies that TOI-1695 is likely to be a slow rotator, older than 1 Gyr. The origin of the supplementary variability in RV and photometry is still uncertain.

The density of TOI-1695 b is 3.6± 1.1 g cm−3, which suggests the existence of an atmospheric layer on top of the solid bulk of the planet. If made of H–He, the gas envelope would have a low mass fraction . If containing water and varying its core composition, the planet may have a water mass fraction of 23±12%. This confirms that TOI-1695 b lies in the sub-Neptune domain of the radius valley.

The detection prospects of the atmosphere of TOI-1695 b with current and future instruments, such as the JWST and ELTs, with a TSM of 48 are relatively encouraging. Its atmosphere has a S/N of detection lower than several other similar targets, and its characterization is thus not the easiest. Nevertheless, its radius and orbital period compared to the rocky-to-gaseous bounds of the different formation and evolution scenarios of super-Earths and sub-Neptunes make it an interesting case for testing these scenarios.

Acknowledgements

The authors gratefully thank the anonymous referee for his corrections and suggestions that led to improve the content of this article. The authors wish to recognize and acknowledge the very significant cultural role and reverence that the summit of MaunaKea has always had within the indigenous Hawaiian community. We are most fortunate to have the opportunity to conduct observations from this mountain. This paper includes data collected by the TESS mission, which are publicly available from the Mikulski Archive for Space Telescopes (MAST). Funding for the TESS mission is provided by NASA’s Science Mission directorate. S.T.Sc.I. is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS 5-26555. We acknowledge funding from the French National Research Agency (ANR) under contract number ANR18CE310019 (SPlaSH). F.K. acknowledges support from the Université Paris Sciences et Lettres under the DIM-ACAV program Origines et conditions d’apparition de la vie. E.M. acknowledges funding from the Fundaçâo de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG) under the project number APQ-02493-22. J.-F.D. acknowledges funding from the European Research Council (ERC) under the H2020 research & innovation programme (grant agreement #740651 NewWorlds). This work is partly supported by the French National Research Agency in the framework of the Investissements d’Avenir program (ANR-15-IDEX-02), through the funding of the “Origin of Life” project of the Grenoble-Alpes University. J.H.C.M. is supported in the form of a work contract funded by Fundaçâo para a Ciência e Tecnologia (FCT) with the reference DL 57/2016/CP1364/CT0007; and also supported from FCT through national funds and by FEDER-Fundo Europeu de Desenvolvimento Regional through COMPETE2020-Programa Operacional Competitividade e Internacionalização for these grants UIDB/04434/2020 & UIDP/04434/2020, PTDC/FIS-AST/32113/2017 & POCI-01-0145-FEDER-032113, PTDC/FIS-AST/28953/2017 & POCI-01-0145-FEDER-028953, PTDC/FIS-AST/29942/2017. J.H.C.M. also acknowleges the support from FCT – Fundaçâo para a Ciência e a Tecnologia through national funds and by FEDER through COMPETE2020 – Programa Operacional Competitividade e Internacionalizaçâo by these grants: UID/FIS/04434/2019; UIDB/04434/2020; UIDP/04434/2020; PTDC/FIS-AST/32113/2017 & POCI-01-0145-FEDER-032113; PTDC/FIS-AST/28953/2017 & POCI-01-0145-FEDER-028953.

Appendix A TESS aperture and field of view

thumbnail Fig. A.1

Aperture used to retrieve the photometry of TOI-1695 from the SPOC in sector 18, compared to the background sources identified by Gaia within 200″ (left), and the background resolved sources on the full-frame image (right). This image is an output from TRICERATOPS (see text).

Appendix B Spectral model of the star

thumbnail Fig. B.1

Stellar line spectral fitting with the MARCS model (see Section 3 for explanations), with CO (top), Mn (bottom left), Na (bottom middle), and OH (bottom right) lines. The best-fitting model is plotted as a green solid line.

Appendix C SPIRou polarimetry time series

thumbnail Fig. C.1

Longitudinal magnetic field variations (red points). A Gaussian process best fit with a period of 48 d with a decay time of 500 days and a smoothing factor commonly fixed to 0.6 days is also shown in blue.

Appendix D Periodograms of RV, FWHM, and BIS obtained with APERO v7

thumbnail Fig. D.1

Same as Fig. 6, but obtained with APERO v7.

Appendix E Corner plot of the MCMC posterior distributions for the transit and RV fit

thumbnail Fig. E.1

MCMC fit posteriors of SPIRou and TESS data. The scheme used is where RVs are drift corrected (DC) and zero-point corrected (ZPC), and a GP with PGP=14.8 days is fitted from the RVs and the light curve. The blue solid line indicates the median of the posterior probability density function (pdf), and the black dotted lines locate the 16 and 84 percentiles of the posterior pdf.

Appendix F Observation logs

Table F.1

Log of SPIRou observations of TOI-1695. The dagger (†) indicates data that were excluded from the drift corrected data reduced with APERO v6. The star (*) indicates data removed from the drift corrected and zero point corrected data reduced with APERO v7 (see discussion in Section 2).

Appendix G RV and polarimetry data tables

Table G.1

SPIRou LBL RV observations of TOI-1695 obtained with APERO v6. The D3V is the third derivative of LBL profiles with respect to radial velocity. It is a proxy for the BIS variations (see text for explanation).

Table G.2

SPIRou LBL RV observations of TOI-1695 obtained with APERO v7.

Table G.3

Bi and null polarization N observations of TOI−1695.

References

  1. Aguichine, A., Mousis, O., Deleuil, M., & Marcq, E. 2021, ApJ, 914, 84 [NASA ADS] [CrossRef] [Google Scholar]
  2. Aigrain, S., Llama, J., Ceillier, T., et al. 2015, MNRAS, 450, 3211 [Google Scholar]
  3. Ambikasaran, S., Foreman-Mackey, D., Greengard, L., Hogg, D. W., & O’Neil, M. 2015, IEEE Trans. Pattern Anal. Mach. Intell., 38, 252 [Google Scholar]
  4. Angus, R., Morton, T., Aigrain, S., Foreman-Mackey, D., & Rajpaul, V. 2018, MNRAS, 474, 2094 [Google Scholar]
  5. Artigau, É., Astudillo-Defru, N., Delfosse, X., et al. 2014, SPIE Conf. Ser., 9149, 914905 [NASA ADS] [Google Scholar]
  6. Artigau, É., Cadieux, C., Cook, N. J., et al. 2022, AJ, 164, 84 [NASA ADS] [CrossRef] [Google Scholar]
  7. Asplund, M., Grevesse, N., Sauval, A. J., & Scott, P. 2009, ARA&A, 47, 481 [NASA ADS] [CrossRef] [Google Scholar]
  8. Basri, G., Walkowicz, L. M., Batalha, N., et al. 2011, AJ, 141, 20 [NASA ADS] [CrossRef] [Google Scholar]
  9. Bellm, E. C., Kulkarni, S. R., Graham, M. J., et al. 2019, PASP, 131, 018002 [Google Scholar]
  10. Benneke, B., Wong, I., Piaulet, C., et al. 2019, ApJ, 887, L14 [Google Scholar]
  11. Blanco-Cuaresma, S. 2019, MNRAS, 486, 2075 [Google Scholar]
  12. Bouchy, F., Pepe, F., & Queloz, D. 2001, A&A, 374, 733 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  13. Cadieux, C., Doyon, R., Plotnykov, M., et al. 2022, AJ, 164, 96 [NASA ADS] [CrossRef] [Google Scholar]
  14. Ciardi, D. R., Beichman, C. A., Horch, E. P., & Howell, S. B. 2015, ApJ, 805, 16 [NASA ADS] [CrossRef] [Google Scholar]
  15. Claret, A. 2017, A&A, 600, A30 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  16. Claret, A., & Southworth, J. 2022, A&A, 664, A128 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  17. Cloutier, R., & Menou, K. 2020, AJ, 159, 211 [NASA ADS] [CrossRef] [Google Scholar]
  18. Cutri, R.-M., Skrutskie, M.-F., van Dyk, S., et al. 2003, in 2MASS All-Sky Point Source Catalog, NASA/IPAC Infrared Science Archive [Google Scholar]
  19. Cointepas, M., Almenara, J. M., Bonfils, X., et al. 2021, A&A, 650, A145 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  20. Cook, N. J., Artigau, É., Doyon, R., et al. 2022, PASP, 134, 114509 [NASA ADS] [CrossRef] [Google Scholar]
  21. Cristofari, P. I., Donati, J. F., Masseron, T., et al. 2022a, MNRAS, 516, 3802 [NASA ADS] [CrossRef] [Google Scholar]
  22. Cristofari, P. I., Donati, J. F., Masseron, T., et al. 2022b, MNRAS, 511, 1893 [NASA ADS] [CrossRef] [Google Scholar]
  23. Donati, J. F., Semel, M., Carter, B. D., Rees, D. E., & Collier Cameron, A. 1997, MNRAS, 291, 658 [Google Scholar]
  24. Donati, J. F., Yu, L., Moutou, C., et al. 2017, MNRAS, 465, 3343 [Google Scholar]
  25. Donati, J. F., Kouach, D., Moutou, C., et al. 2020, MNRAS, 498, 5684 [Google Scholar]
  26. Engle, S. G., & Guinan, E. F. 2018, Res. Notes Am. Astron. Soc., 2, 34 [Google Scholar]
  27. Foreman-Mackey, D., Hogg, D. W., Lang, D., & Goodman, J. 2013, PASP, 125, 306 [Google Scholar]
  28. Fraine, J., Deming, D., Benneke, B., et al. 2014, Nature, 513, 526 [Google Scholar]
  29. Francesco, A., Mario, D., Li, Z., & Alessandro, S. 2022, Res. Notes Am. Astron. Soc., 6, 28 [Google Scholar]
  30. Fulton, B. J., & Petigura, E. A. 2018, AJ, 156, 264 [Google Scholar]
  31. Fulton, B. J., Petigura, E. A., Howard, A. W., et al. 2017, AJ, 154, 109 [Google Scholar]
  32. Furlan, E., Ciardi, D. R., Everett, M. E., et al. 2017, AJ, 153, 71 [NASA ADS] [CrossRef] [Google Scholar]
  33. Gaia Collaboration (Brown, A.G.A. et al.) 2021, A&A, 649, A1 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  34. Giacalone, S., & Dressing, C. D. 2020, Astrophysics Source Code Library [record ascl:2002.004] [Google Scholar]
  35. Giacalone, S., Dressing, C. D., Jensen, E. L. N., et al. 2021, AJ, 161, 24 [Google Scholar]
  36. Ginzburg, S., Schlichting, H. E., & Sari, R. 2018, MNRAS, 476, 759 [Google Scholar]
  37. Goulding, N. T. 2013, PhD thesis, University of Hertfordshire, UK [Google Scholar]
  38. Goulding, N. T., Barnes, J. R., Pinfield, D. J., et al. 2012, MNRAS, 427, 3358 [NASA ADS] [CrossRef] [Google Scholar]
  39. Günther, M. N., Pozuelos, F. J., Dittmann, J. A., et al. 2019, Nat. Astron., 3, 1099 [Google Scholar]
  40. Gupta, A., Nicholson, L., & Schlichting, H. E. 2022, MNRAS, 516, 4585 [NASA ADS] [CrossRef] [Google Scholar]
  41. Gustafsson, B., Edvardsson, B., Eriksson, K., et al. 2008, A&A, 486, 951 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  42. Hauschildt, P. H., Allard, F., & Baron, E. 1999, ApJ, 512, 377 [Google Scholar]
  43. Haywood, R. D., Collier Cameron, A., Queloz, D., et al. 2014, MNRAS, 443, 2517 [Google Scholar]
  44. Hobson, M. J., Bouchy, F., Cook, N. J., et al. 2021, A&A, 648, A48 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  45. Jenkins, J. M., Twicken, J. D., McCauliff, S., et al. 2016, SPIE Conf. Ser., 9913, 99133E [Google Scholar]
  46. Kanodia, S., & Wright, J. 2018, Res. Notes Am. Astron. Soc., 2, 4 [Google Scholar]
  47. Kempton, E. M. R., Bean, J. L., Louie, D. R., et al. 2018, PASP, 130, 114401 [Google Scholar]
  48. Kite, E. S., & Schaefer, L. 2021, ApJ, 909, L22 [NASA ADS] [CrossRef] [Google Scholar]
  49. Kochanek, C. S., Shappee, B. J., Stanek, K. Z., et al. 2017, PASP, 129, 104502 [Google Scholar]
  50. Kosiarek, M. R., Crossfield, I. J. M., Hardegree-Ullman, K. K., et al. 2019, AJ, 157, 97 [NASA ADS] [CrossRef] [Google Scholar]
  51. Kovács, G., Zucker, S., & Mazeh, T. 2002, A&A, 391, 369 [Google Scholar]
  52. Kreidberg, L. 2015, PASP, 127, 1161 [Google Scholar]
  53. Lecavelier Des Etangs, A. 2007, A&A, 461, 1185 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  54. Lee, E. J., & Connors, N. J. 2021, ApJ, 908, 32 [NASA ADS] [CrossRef] [Google Scholar]
  55. Lee, E. J., Chiang, E., & Ormel, C. W. 2014, ApJ, 797, 95 [Google Scholar]
  56. Li, J., Tenenbaum, P., Twicken, J. D., et al. 2019, PASP, 131, 024506 [NASA ADS] [CrossRef] [Google Scholar]
  57. Lightkurve Collaboration (Cardoso, J.V.D.M., et al.) 2018, Astrophysics Source Code Library [record ascl:1812.013] [Google Scholar]
  58. Lopez, E. D., & Fortney, J. J. 2014, ApJ, 792, 1 [Google Scholar]
  59. Lopez, E. D., & Rice, K. 2018, MNRAS, 479, 5303 [NASA ADS] [CrossRef] [Google Scholar]
  60. Mann, A. W., Dupuy, T., Kraus, A.L., et al. 2019, ApJ, 871, 63 [NASA ADS] [CrossRef] [Google Scholar]
  61. Martinez, C. F., Cunha, K., Ghezzi, L., & Smith, V. V. 2019, ApJ, 875, 29 [Google Scholar]
  62. Martioli, E., Hébrard, G., Moutou, C., et al. 2020, A&A, 641, L1 [EDP Sciences] [Google Scholar]
  63. Martioli, E., Hébrard, G., Fouqué, P., et al. 2022, A&A, 660, A86 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  64. Masci, F. J., Laher, R. R., Rusholme, B., et al. 2019, PASP, 131, 018003 [Google Scholar]
  65. McQuillan, A., Aigrain, S., & Roberts, S. 2012, A&A, 539, A137 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  66. Mikal-Evans, T., Crossfield, I. J. M., Benneke, B., et al. 2021, AJ, 161, 18 [Google Scholar]
  67. Montegriffo, P., De Angeli, F., Andrae, R., et al. 2023, A&A, in press, https://doi.org/10.1051/0004-6361/202243880 [Google Scholar]
  68. Moutou, C., Hébrard, E. M., Morin, J., et al. 2017, MNRAS, 472, 4563 [NASA ADS] [CrossRef] [Google Scholar]
  69. Muirhead, P. S., Dressing, C. D., Mann, A. W., et al. 2018, AJ, 155, 180 [NASA ADS] [CrossRef] [Google Scholar]
  70. Owen, J. E., & Wu, Y. 2013, ApJ, 775, 105 [Google Scholar]
  71. Passegger, V. M., Schweitzer, A., Shulyak, D., et al. 2019, A&A, 627, A161 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  72. Peterson, M. S., Benneke, B., David, T. J., et al. 2018, AJ, 156, 188 [NASA ADS] [CrossRef] [Google Scholar]
  73. Piaulet, C., Benneke, B., Rubenzahl, R. A., et al. 2021, AJ, 161, 70 [NASA ADS] [CrossRef] [Google Scholar]
  74. Piette, A. A. A., & Madhusudhan, N. 2020, ApJ, 904, 154 [NASA ADS] [CrossRef] [Google Scholar]
  75. Plez, B. 2012, Astrophysics Source Code Library [record ascl:1205.004] [Google Scholar]
  76. Polanski, A. S., Crossfield, I. J. M., Burt, J. A., et al. 2021, AJ, 162, 238 [NASA ADS] [CrossRef] [Google Scholar]
  77. Ricker, G. R., Winn, J. N., Vanderspek, R., et al. 2015, J. Astron. Teles. Instrum. Syst., 1, 014003 [Google Scholar]
  78. Rogers, J. G., & Owen, J. E. 2021, MNRAS, 503, 1526 [NASA ADS] [CrossRef] [Google Scholar]
  79. Schlegel, D. J., Finkbeiner, D. P., & Davis, M. 1998, ApJ, 500, 525 [Google Scholar]
  80. Schneider, J., Dedieu, C., Le Sidaner, P., Savalle, R., & Zolotukhin, I. 2011, A&A, 532, A79 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  81. Shappee, B. J., Prieto, J. L., Grupe, D., et al. 2014, ApJ, 788, 48 [Google Scholar]
  82. Shuvalov, V. 2009, MAPS, 44, 1095 [NASA ADS] [Google Scholar]
  83. Smith, J. C., Stumpe, M. C., Van Cleve, J. E., et al. 2012, PASP, 124, 1000 [Google Scholar]
  84. Smith, V. V., Bizyaev, D., Cunha, K., et al. 2021, AJ, 161, 254 [NASA ADS] [CrossRef] [Google Scholar]
  85. Sneden, C., Bean, J., Ivans, I., Lucatello, S., & Sobeck, J. 2012, Astrophysics Source Code Library [record ascl:1202.009] [Google Scholar]
  86. Southworth, J. 2011, MNRAS, 417, 2166 [Google Scholar]
  87. Stassun, K. G., & Torres, G. 2016, AJ, 152, 180 [Google Scholar]
  88. Stassun, K. G., & Torres, G. 2021, ApJ, 907, L33 [NASA ADS] [CrossRef] [Google Scholar]
  89. Stassun, K. G., Collins, K. A., & Gaudi, B. S. 2017, AJ, 153, 136 [Google Scholar]
  90. Stassun, K. G., Corsaro, E., Pepper, J. A., & Gaudi, B. S. 2018, AJ, 155, 22 [Google Scholar]
  91. Stassun, K. G., Oelkers, R. J., Paegert, M., et al. 2019, AJ, 158, 138 [Google Scholar]
  92. Stumpe, M. C., Smith, J. C., Catanzarite, J. H., et al. 2014, PASP, 126, 100 [Google Scholar]
  93. Tsiaras, A., Waldmann, I. P., Tinetti, G., Tennyson, J., & Yurchenko, S. N. 2019, Nat. Astron., 3, 1086 [Google Scholar]
  94. Twicken, J. D., Catanzarite, J. H., Clarke, B. D., et al. 2018, PASP, 130, 064502 [Google Scholar]
  95. Van Eylen, V., Agentoft, C., Lundkvist, M. S., et al. 2018, MNRAS, 479, 4786 [Google Scholar]
  96. Van Eylen, V., Astudillo-Defru, N., Bonfils, X., et al. 2021, MNRAS, 507, 2154 [NASA ADS] [Google Scholar]
  97. Wizinowich, P., Acton, D. S., Shelton, C., et al. 2000, PASP, 112, 315 [NASA ADS] [CrossRef] [Google Scholar]
  98. Wright, J. T., & Eastman, J. D. 2014, PASP, 126, 838 [Google Scholar]
  99. Wright, E. L., Eisenhard, P. R. M., Mainzer, A. K., et al. 2010, AJ, 140, 1868 [NASA ADS] [CrossRef] [Google Scholar]
  100. Wu, Y. 2019, ApJ, 874, 91 [NASA ADS] [CrossRef] [Google Scholar]
  101. Zeng, L., Sasselov, D. D., & Jacobsen, S. B. 2016, ApJ, 819, 127 [Google Scholar]
  102. Zeng, L., Jacobsen, S. B., Hyung, E., et al. 2021, ApJ, 923, 247 [NASA ADS] [CrossRef] [Google Scholar]

2

APERO does not extract the bluest order due to low S/N.

All Tables

Table 1

Main astrometric and photometric data of TOI-1695.

Table 2

Spectral parameters of the star.

Table 3

Prior distribution of fitted or fixed parameters entering the MCMC sampling.

Table 4

MCMC results of the photometry (TESS) and spectroscopy (SPIRou) joint fit.

Table F.1

Log of SPIRou observations of TOI-1695. The dagger (†) indicates data that were excluded from the drift corrected data reduced with APERO v6. The star (*) indicates data removed from the drift corrected and zero point corrected data reduced with APERO v7 (see discussion in Section 2).

Table G.1

SPIRou LBL RV observations of TOI-1695 obtained with APERO v6. The D3V is the third derivative of LBL profiles with respect to radial velocity. It is a proxy for the BIS variations (see text for explanation).

Table G.2

SPIRou LBL RV observations of TOI-1695 obtained with APERO v7.

Table G.3

Bi and null polarization N observations of TOI−1695.

All Figures

thumbnail Fig. 1

Full TESS light curve separated in sectors (gray dots) and binned with a 0.1-day timestep (black). The identified transit locations are shown as orange vertical lines.

In the text
thumbnail Fig. 2

Periodogram of the TESS SPOC light curve with a peak at ~15 days for a 175 ppm signal.

In the text
thumbnail Fig. 3

Companion sensitivity for the Keck NIR adaptive optics imaging. The black points represent the 5σ limits and are separated in steps of 1 FWHM (~0.054″); purple represents the azimuthal dispersion (1σ) of the contrast determinations (see text). The inset image is of the primary target showing no additional companions to within 3″ of the target.

In the text
thumbnail Fig. 4

Spectral energy distribution of TOI-1695. Red symbols represent the observed photometric measurements, where the horizontal bars represent the effective width of the passband. Blue symbols are the model fluxes from the best-fit NextGen atmosphere model (black curve). The green curve is the published Gaia DR3 spectrum for this star.

In the text
thumbnail Fig. 5

Periodogram of the longitudinal magnetic field variations, with dominant frequencies highlighted in blue. The window function is shown in orange, with a peak close to the synodic orbital period of the Moon.

In the text
thumbnail Fig. 6

Lomb-Scargle periodograms with FAP levels 0.01 and 0.05 indicated as dotted and dashed lines for data extracted with the APERO v6 version: (from top to bottom) RV, FWHM, and BIS. They are compared to the window function (red solid line). The vertical orange solid line indicates the 3.134-day period of the TESS transit signal. The moon synodic orbital period (29.53 days) is also shown as a green solid line.

In the text
thumbnail Fig. 7

Periodogram of the residuals O-C after fitting a 3.134-day Keplerian to the RVs. Same color-coding as in Fig. 6.

In the text
thumbnail Fig. 8

Gaussian process fit of the light curve with a quasi-periodic kernel. Gray points are raw PDCSAP flux TESS data, and black points are the binned data with Δtbin = 0.25 days.

In the text
thumbnail Fig. 9

Gaussian process fit of the SPIRou RV in the case of the APERO v6 reduction (top) and the v7 reduction (bottom) with a quasi-periodic kernel.

In the text
thumbnail Fig. 10

Results from fits to the TESS light curve, with all the observed transits stacked, with the model taken from the maximum a posteriori point estimation, based on maximizing the posterior probability distributions for each parameter. At this point, T0 = 1791.5206 BTJD, Ρ = 3.1342799 days, a = 21.1 R*, Rp = 0.034 R*, and Ip = 89.4°.

In the text
thumbnail Fig. 11

Phase-folded model of the RV variation seen with SPIRou of the star TOI-1695 with a 3.13-day circular orbit. The data used here are derived from APERO v7. Gray dots gather all the RV data and black dots are binned with a time step of 0.05 days.

In the text
thumbnail Fig. 12

Mass-radius plot obtained based on the pyExoRaMa code (Zeng et al. 2021; Francesco et al. 2022). The red, green, and blue solid lines represent the mass-radius relation for respectively pure Fe, silicate, and H2O core. The yellow-brown z-contours (see Zeng et al. 2021 for details) with black solid lines represent the radius inflation due to gas in an envelope surrounding the planet core corresponding to pure silicate. The comparison planets (black circles) are taken from the TepCat database (Southworth 2011), selecting only exoplanets around M and Κ host stars. TOI-1695 b is shown as a blue star.

In the text
thumbnail Fig. 13

Rp-Porb diagram for exoplanets around cool stars (Teff < 4000 K) taken from the NASA exoplanet archive. The solid and dashed lines represent respectively the Cloutier & Menou (2020) and Martinez et al. (2019) empirical radius valley (see text). The shaded region shows exoplanets that from their gas content could be able to test gas-depleted formation and thermally driven mass loss scenarios. TOI-1695 b is highlighted as a thick-lined black circle.

In the text
thumbnail Fig. 14

Signal-to-noise ratio (left axis) of the atmospheric signatures and TSM (right axis) in the J-band as a function of the planetary mass for exoplanets orbiting M-type stars with masses between 2 and 10 Earth mass. The S/N are normalized to a reference S/N of 100 for AU Mic b, as in Martioli et al. (2022).

In the text
thumbnail Fig. A.1

Aperture used to retrieve the photometry of TOI-1695 from the SPOC in sector 18, compared to the background sources identified by Gaia within 200″ (left), and the background resolved sources on the full-frame image (right). This image is an output from TRICERATOPS (see text).

In the text
thumbnail Fig. B.1

Stellar line spectral fitting with the MARCS model (see Section 3 for explanations), with CO (top), Mn (bottom left), Na (bottom middle), and OH (bottom right) lines. The best-fitting model is plotted as a green solid line.

In the text
thumbnail Fig. C.1

Longitudinal magnetic field variations (red points). A Gaussian process best fit with a period of 48 d with a decay time of 500 days and a smoothing factor commonly fixed to 0.6 days is also shown in blue.

In the text
thumbnail Fig. D.1

Same as Fig. 6, but obtained with APERO v7.

In the text
thumbnail Fig. E.1

MCMC fit posteriors of SPIRou and TESS data. The scheme used is where RVs are drift corrected (DC) and zero-point corrected (ZPC), and a GP with PGP=14.8 days is fitted from the RVs and the light curve. The blue solid line indicates the median of the posterior probability density function (pdf), and the black dotted lines locate the 16 and 84 percentiles of the posterior pdf.

In the text

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.