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A&A
Volume 687, July 2024
Article Number A176
Number of page(s) 24
Section Stellar structure and evolution
DOI https://doi.org/10.1051/0004-6361/202348926
Published online 09 July 2024

© The Authors 2024

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

An interesting phenomenon of the stars on the upper main sequence are the so-called chemically peculiar (CP) stars. First discovered by Maury & Pickering (1897), they still puzzle ustoday. This group of stars not only exhibits over- or underabundances of certain elements, but it was discovered that they could show spectroscopic and photometric variability due to the presence of a magnetic field (Deutsch 1947; Stibbs 1950). Preston (1974) assembled a classification scheme that is still valid today. He introduced four peculiarity subgroups.

The first subgroup, the classical Am (metallic lined, CP1) stars, contains A- to F-type stars whose spectra are different from the typical spectra of similar spectral types in that iron and similar elements are overabundant, while other elements such as calcium and scandium are underabundant. Many of the classical Am stars are found in binary systems, which may contribute to the occurrence of peculiarities that are due to tidal braking. Tidal breaking allows the helium convection zone to settle, which in turn gives way to atomic diffusion to the surface layers (Théado et al. 2005; Abt 2009).

The second (CP2 or Ap) group contains late B- to early F- stars with overabundances of Sr, Si, Cr and Eu. Additionally, many objects exhibit strong magnetic fields up to some dozen kiloGauss (Bychkov et al. 2021). Those strong cause chemical spots around the magnetic poles, and the spots lead to the photometric (the so-called ACV variables) and spectroscopic variability (with periods comparable to the rotation period of the star) that has been described for a plethora of stars (see e.g. Bernhard et al. 2015a,b; Hümmerich et al. 2018). The origin of these magnetic fields is still not entirely clear, but it has been suggested that the fields can be frozen-in during the formation of the star (Moss 2003) and that a field like this can be sustained after the transition from a convective to a radiative core (Schleicher et al. 2023).

The mercury-manganese stars that comprise the third (CP3) group show overabundances of mercury and/or manganese, hence the name. They exhibit no detected magnetic fields, and other than overabundances in iron-peak elements, their atmospheres seem stable. They can be seen as the hot analogues of the CP1 group (Ghazaryan et al. 2018). However, while there seem to be similarities between the two types, a satisfying model that describes the existence of these peculiarities in the CP3 group remains to be found (Adelman et al. 2003).

The fourth group (CP4 or He-weak/He-strong) of stars are B-type stars with an overabundance or lack of helium compared to (apparently) normal stars in the same effective temperature domain. Similarly to the CP2 group, these stars show large-scale magnetic fields, and thus also spectral and photometric variability (Pedersen & Thomsen 1977).

Additionally, one more group is defined in the literature, the so-called λ Böotis stars (e.g. Smith 1996; Paunzen et al. 2014a). The stars in this category show broad hydrogen lines and weak to no lines of heavier elements such as Mg when compared to normal stars at similar temperatures. Many theories to explain these phenomena such as interplay between mass loss and diffusion (Michaud & Charland 1986) or interplay between diffusion and accretion (Venn & Lambert 1990) have been proposed.

After the initial collapse of a molecular coud, a protostar is formed that accretes matter onto its core. When the protostar becomes visible, it arrives at its birthline (e.g. Stahler 1983; Palla & Stahler 1990; Lada 2005). After this, a phase of contraction follows until the core of the star reaches temperatures of ∼107 K, where the hydrogen burning begins. This stage in the evolution is called the pre-main-sequence (PMS) phase. Here, we commonly differ between low-mass T Tauri stars and the medium-mass Herbig Ae/Be stars. When talking about possible PMS-CP stars, we need to focus on the second category.

Herbig Ae/Be stars are intermediate-mass (∼2–10 M⊙) PMS stars of late-O to early-F spectral type that show typical signs of pre-main-sequence evolution: emission in Balmer lines due to accretion and infrared-excess due to the disk of dust and debris around the young star (see Brittain et al. 2023 for an extensive recent review of this matter). These stars are thought to be linked to the CP phenomenon since similar characteristics have already been found in these stars (Folsom et al. 2012).

The mechanisms that cause the CP phenomenon, such as diffusion, rotation, and mass loss strongly depend on time. Therefore, one of the main questions is at which point during the stellar evolution the peculiarities arise. It is therefore natural to search for the youngest objects that already show signs of the phenomena described above.

Only a few pre-main sequence CP (PMS-CP) stars or candidates have been mentioned in the literature so far for example Netopil et al. (2014) presented an intermediate-mass PMS star that already showed Am peculiarities in the open cluster Stock 16. A similar study was performed by Cariddi et al. (2018) for the open cluster Hogg 16, resulting in three PMS-CP candidates. Additionally, Potravnov et al. (2023a) detected a young He-weak star in the star-forming region NGC 1333. Lastly, Potravnov et al. (2023b) reported the discovery of a young CP2 star with a magnetic field with a strength of 3.5 kG.

The recent third data release (DR3) of the measurements from Gaia (Gaia Collaboration 2021) with its precise astrometry is an excellent source for detecting open clusters. Hunt & Reffert (2023) published an extensive catalogue of more than 7000 aggregates based on this dataset.

In this paper, we use the memberships of this catalogue to present data for 45 PMS-CP candidates in star clusters (Figs. 1 and 2).

thumbnail Fig. 1.

Sky distribution of the final 45 targets we used for the further analysis in this work.

thumbnail Fig. 2.

Spectrum of the B8 IV Si (libr18) star Gaia DR3 121406905707934464/LAMOST J032919.94+312457.0. The Si II lines are shown.

2. Target selection

We selected CP1 to CP4 stars from eight references and excluded no objects. The stars were first identified in the latest Gaia release. We found several duplicates, which were then eliminated from the sample. This left us with about 29 300 objects from the following papers (ordered chronologically).

Renson & Manfroid (2009): the authors started to collect and publish CP stars and candidates in the late 1980s. The last edition used for our purpose includes 8205 known or suspected CP stars. This catalogue remains the basis for almost all studies in this research field. However, the authors included all stellar objects listed at least once as peculiar. It is a rather uncritical compilation that must be treated cautiously.

Netopil (2013): the thesis includes magnetic CP stars in open clusters with membership probabilities deduced before the Gaia data became available. The high quality of this analysis is proven by the consistency of the conclusions when comparing the newest astrometric data.

Qin et al. (2019): using spectra from the LAMOST DR5 (Cui et al. 2012) with a signal-to-noise ratio higher than 50, 9372 CP1 stars and 1131 CP2 candidates were compiled into a catalogue. The authors used six machine-learning algorithms incorporating known CP1 spectra. They concluded that the random forest (RF) algorithm selected CP stars best. Furthermore, they also manually identified them based on the spectral features derived from the RF algorithm.

Hümmerich et al. (2020): they presented 1002 magnetic CP stars selected by searching LAMOST DR4 spectra for the characteristic 5200 Å flux depression. The spectral classification was made with a modified version of the MKCLASS code (Gray et al. 2016). As the final step, the accuracy of the automatic classifications was estimated by comparison with results from manual classification and the literature. This guaranteed the best possible spectral type.

Chojnowski et al. (2020): they reported 260 newly identified CP3 stars based on H-band spectra obtained via the Sloan Digital Sky Survey (SDSS) Apache Point Observatory Galactic Evolution Experiment (APOGEE) survey (Majewski et al. 2017). The CP3 stars were identified among the telluric standard stars as those whose metallic absorption content is limited to or dominated by the H-band Mn II lines.

Paunzen et al. (2021a): as done in Hümmerich et al. (2020), they searched for among pre-selected early-type spectra from LAMOST DR4 using a modified version of the MKCLASS code which probes several Hg II and Mn II features. The spectra of the resulting 332 candidates were visually inspected.

Shang et al. (2022): similar to Qin et al. (2019), they applied three machine-learning algorithms to search for CP1 and CP2 stars within LAMOST DR8 spectra. However, they selected the XGBoost algorithm (Li et al. 2019) as the most efficient. Their catalogue comprises 6917 and 1652 newly discovered CP1 and CP2 stars, respectively.

Shi et al. (2023): they applied the same techniques as Hümmerich et al. (2020) for the LAMOST DR9 but without any critical assessment of the spectral types.

3. Matching with members of open clusters

The matching was made via Gaia IDs, which had to be deduced for our CP star sample. The published coordinates and identifications were cross-checked within a certain matching radius. Components of binary systems were all verified manually.

We used the clusters member list and membership probabilities by Hunt & Reffert (2023) to match our list of bona-fide CP stars. They applied the algorithm called Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN, McInnes et al. 2017) to recover star clusters. They validated the aggregates they found using a statistical density test and a Bayesian convolutional neural network for classification in a colour-magnitude diagram. In addition, this catalogue contains the parameters (age, reddening, and distance) of 7167 star clusters, with more than 700 newly discovered high-confidence star clusters. We must emphasize that determining the cluster parameters is still challenging, although a reasonable distance estimate can be obtained from the Gaia datasets (Netopil et al. 2015; Dias et al. 2021). To check their results, we compared them with the catalogues by Cantat-Gaudin et al. (2020) and Dias et al. (2021). The results generally agree. Some outliers have been described in Hunt & Reffert (2023).

We cross-matched our sample with the catalogue of star clusters and identified 682 high-confidence (P > 0.7) members within 460 aggregates. As the next step, we checked for all members whether they might be PMS objects or close to the zero-age main sequence (ZAMS). To do this, we used appropriate isochrones (Bressan et al. 2012) and the location of the stars in the corresponding colour-magnitude diagrams. Initially, only star clusters with ages younger than 50 Myr were chosen. Finally, we identified 45 high-confidence (P > 0.7) members within 39 young star clusters.

4. Δa photometry

The Δa photometry tool is potent in investigating CP stars (Paunzen et al. 2005). It investigates the flux depression at 5200 Å a spectral feature that only occurs in CP stars. The photometric system samples the depth of this feature by comparing the flux at the centre with the adjacent regions using bandwidths of 110–230 Å. The flux depression is caused by line blanketing of Cr, Fe, and Si in this region, which is enhanced by a magnetic field (Kupka et al. 2003; Khan & Shulyak 2007). This is most significantly visible in magnetic CP stars. However, not all these objects show this flux depression, probably for observational reasons (an unfavourable inclination) and magnetic field characteristics (Paunzen et al. 2005). In addition, some (non- or only weakly magnetic) CP1 and CP3 also show detectable positive Δa values but with a far lower significance.

Galactic field stars and open cluster fields alone were surveyed so far (Netopil et al. 2007; Paunzen et al. 2014b). Another approach to the Δa photometry was presented by Paunzen & Prišegen (2022), who used Gaia BP/RP spectra for the synthesis. They found a detection level of more than 85% for almost the entire investigated spectral range of the upper main sequence. In total, 597 of the cluster members have an available BP/RP spectrum.

5. Astrophysical parameters

For the statistical analysis, we need the astrophysical parameters of the stars (Teff, log g or luminosity, and mass). The homogeneity of these parameters is most important for draw the correct conclusions. We used the correlations taken from Paunzen (2024), who analysed Teff and log g values of different automatic pipelines (Anders et al. 2019, 2022; Stassun et al. 2019; Fouesneau et al. 2023; Zhang et al. 2023) for the four CP star subgroups. He derived mean uncertainties between 3 and 12%.

We used the isochrones by Bressan et al. (2012) to do this. They include a PMS phase starting with log t ≥ 6.6.

6. Light curves

6.1. TESS

Since many stars in the CP2/4 subgroups show variability in their brightness and spectra, a phenomenon explained by the oblique rotator theory (Stibbs 1950), it is only natural to search for variabilities like this in our stars. The main classes to search for are the α2 Canum Venaticorum (ACV), SX Arietis (SXARI), and the rapidly oscillating Ap (roAp) stars.

We used the Python package eleanor1 (Feinstein et al. 2019) to download light curves collected by the Transiting Exoplanet Survey Satellite (TESS) mission (Ricker et al. 2015). The light curves of 33 of our 45 stars were accessible via this method, some even in multiple sectors. After extraction, outliers were removed using a 3σ cut on the flux data. Subsequently, the flux differences were converted into relative magnitudes using the well-known relation

Δ mag = − 2.5 log 10 ( I I 0 ) . $$ \begin{aligned} \Delta \mathrm{mag} = -2.5\log _{10}\left(\frac{I}{I_0}\right). \end{aligned} $$(1)

However, these light curves have to be used with caution because in TESS, a relatively large portion of the sky (21″ × 21″) falls onto each pixel, which may lead to blending with other sources in this area. Thus, the signal cannot be entirely assumed to come from the target star and not a nearby source. An example is the star Gaia DR3 3131891973309856640, whose signal likely comes from the nearby star V640 Mon (HD 47088). The light curve is shown in Fig. B.17.

Finally, frequency analysis was performed using a Lomb-Scargle periodogram Lomb (1976), Scargle (1982). The light curves were then phase-folded at the most prominent period.

The light curve data from this method had some issues regarding quality and consistency, thus we could not find a good solution in the frequency analysis for all stars. However, the data were good enough for 13 stars to allow for more or less periodic signals to be seen in the light curves. The results of this analysis are presented in Appendix B.

Based on the method, the light curve data are processed by eleanor, and some long-term variability might be lost. This is prominent in the case of Gaia DR3 121406905707934464, which has a rotation period of 123.3 d, but probably due to the detrending of the light curve, this signal was lost for our analysis (Fig. B.1).

6.2. CoRoT

The light curve of one star (Gaia DR3 3326696507149141120) is reported in the database of the COnvection, ROtation and planetary Transits (CoRoT) satellite. The observations span roughly three weeks. The light curve was treated in the same way as the data obtained from the TESS satellite.

7. Spectral classification

Tthe CP nature of the stars is best confirmed using spectroscopy. We searched the latest public data release (DR92 of the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST, Cui et al. 2012; Zhao et al. 2012) for spectral data. Spectra were available for four stars and could therefore be used for classification. To do this, we used the code MKCLASS (Gray & Corbally 2014), which is an automatic routine that compares the spectra to spectral libraries and determines the best-fitting spectral type according to the quality of the spectra.

On the rectified spectra, we used the spectral libraries libr18_225, libr18 and libnor36 to classify the spectra.

Additionally, we searched the ESO database3 for spectra of our stars. Spectral data in a suitable wavelength range for spectral classification were found for seven sources. They either had spectra from the Ultraviolet and Visual Echelle Spectrograph (UVES, Dekker et al. 2000, four stars) or from X-shooter (Vernet et al. 2011, two stars) and one star had a spectrum available from the High Accuracy Radial velocity Planet Searcher (HARPS, Mayor et al. 2003).

We note that the signal-to-noise ratio in the g-band of the LAMOST spectra is not the best for the fainter sources. The automatic classification can therfore vary depending on which library of standard stars is used. Since the number of stars with available spectra is small, we also inspected the spectra by eye to verify the automatic classification and to provide a more accurate result based on the data.

8. Spectral energy distributions

To confirm the PMS status of our target stars, that is, to search for infrared excess, and to obtain proper estimates of the stellar parameters, we fitted spectral energy distributions to available photometric data. We used the Python package astroARIADNE (Vines & Jenkins 2022)4 to search for photometric data which is available online and fit an atmospheric model (in this case the models from Kurucz 1993; Castelli 2003) to the photometry. After fitting the models, the package uses Bayesian model averaging (BMA) to find the best-fitting stellar parameters, such as the effective temperature, stellar radius, surface gravity, and luminosity. Additionally, the age of the star can be determined by the use of MIST isochrones (Paxton et al. 2011, 2013, 2015; Dotter 2016; Choi et al. 2016). However, since these isochrones do not cover the pre-main-sequence evolution, the ages determined by the fitting routine are not suitable for our case.

astroARIADNE was unable to find any infrared excess. This is probably a result of the photometric data acquired by the package and the subsequent fitting process. It also provides only the raw SED of the measurements, so no extinction correction is done to infer the actual stellar parameters. Some stars, especially those with higher AV values, therefore have cooler temperatures than they truly have. One notable example is the star SHI261 (Gaia DR3 121406905707934464), a B-type star with an effective temperature of 11 175 ± 130 K according to Potravnov et al. (2023b) who used high-resolution spectroscopy in addition to an SED fit, and determined an effective temperature of only 5587 − 463 + 589 $ 5587^{+589}_{-463} $ K by astroAriadne. However, the final AV values determined by astroARIADNE are all close to zero (see Fig. 3). The results therefore have to be interpreted with caution.

thumbnail Fig. 3.

Comparison of the extinction values given by Hunt & Reffert (2023) and those estimated by astroARIADNE.

The results of the SED fitting were compared with the output from the Virtual Observatory SED Analyser5 (VOSA) (Bayo et al. 2008).

According to the results from VOSA, 15 of our 45 stars had enough data in the near and mid-infrared region to find an IR excess that is commonly associated with debris disks around newly formed stars. An example can is shown in Fig. 4.

thumbnail Fig. 4.

Spectral energy distribution for the star R2175 (Gaia DR3 3326717260430731648) as measured by VOSA (black dots). The red spectrum is a model from Castelli (2003) with the parameters Teff = 13 000 K, log g = 4.0 and [Fe/H]= − 0.5 dex. The vertical green line denotes the effective wavelength of the WISE W2 band from the point at which the measured SED deviates from the model spectrum, and the IR excess starts to show.

9. Results

9.1. Pre-main-sequence stars vs non-pre-main-sequence stars

The results of the SED fitting using VOSA show that 15 of our sample stars exhibit an infrared excess. The measurements suggest that the IR excess starts in the mid-infrared region after 3 μm, meaning that the debris disk around each of these stars has a temperature below approximately 1000 K. Additionally, 2 of our sources show at least one hydrogen line emission in their spectra. One of these 2 sources (Gaia DR3 5943020022195591552) shows no IR excess in its SED (Fig. 5).

thumbnail Fig. 5.

Same as Fig. 4 but here we show the output plot generated by astroARIADNE. The synthetic photometry is shown via the cyan points; the purple diamonds show the fit points. The synthetic spectrum is a spectrum with the parameters Teff = 9500 K, log g = 3.5 and [Fe/H]= − 0.1 dex taken from Kurucz (1993). This method probes a smaller wavelength range so that no IR excess can be seen here.

9.2. Variability

Thirteen of the 33 light curves available from TESS showed clear variability in accordance to them being CP stars. We found 6 definitive ACV variables with a double wave (see Appendix B). Eight can be classified as having rotating features or binarity in their data. The light curve of one star, Gaia DR3 5541472465805985024, shows a periodic pattern with a period of 4.241 days, which likely is due to rotation. It also shows a higher frequency pulsational pattern, which probably is a pattern commonly found in γ Doradus variables or PMS stars (Fig. B.29).

Seven of the remaining light curves show no or irregular variability. They are treated as VAR because some variability of unknown origin is visible. These patterns in variability, for instance the irregular ones, could also be signs of the PMS evolution of the star. The last group, suspected binary stars, consists of three sources, of which for one, Gaia DR3 3131891973309856640, the signal is more likely to come from the nearby known variable V649 Mon (Fig. B.17; see also Table B.1 for the variability classification of the whole sample based on TESS light curves).

9.3. Spectral Types

9.3.1. LAMOST

The spectral types of the four availaable LAMOST spectra were determined by MKCLASS. The result is listedx in Table 1. We note that the signal-to-noise ratios of the spectra are not particularly good, so the automatically detected spectral types are somewhat unsure. However, a manual inspection can help in these cases. One of the spectra (Gaia DR3 3368982075084757632) shows emission in the Hα line, which is a clear sign of the PMS status of the star. This star is also a CP star candidate without previous mention in the literature. Table 1 lists the sample of the stars with LAMOST spectra, the classifications made by MKCLASS, and the visual inspection.

Table 1.

Spectral types determined by MKCLASS from the LAMOST spectra.

9.3.2. ESO spectra

Due to the high spectral resolution and/or a spectral range that was unsuitable for MKCLASS, ESO stars were classified manually. Four of the seven stars showed signs of chemical peculiarity. The others have been classified as CP or CP candidates in the literature (see Table 2).

Table 2.

Same as Table 1 but for the seven stars with spectra available from the ESO archive.

9.4. Fraction of chemically peculiar stars

We determined in our sample that are CP stars and those that have the possibility of being normal stars. The results for the spectral classification are given in Tables 1 and 2. Many of these starsx can be considered CP stars or CP candidates according to our classification and the classification found in the literature. Not every source has a definite CP spectral type in the literature (especially in Renson & Manfroid 2009). Based on the entire sample and by comparing it to the spectral types found in the literature, 29 of our 45 (64%) candidates can be considered CP stars. The others either lack a spectral type or have only vague spectral classifications, such as A1–A7 for the star Gaia DR3 2204463918269731072 (see Table C.1).

No spectral type is listed for Gaia DR3 3368982075084757632, but it has a LAMOST spectrum showing possible signs of Si and/or Sr/Eu peculiarity (see Fig. 6) However, the spectrum has a signal-to-noise ratio (S/N) of only 47.77 in the g-band, so the exact spectral type is unclear, whereas the flux depression around 5200 Å is relatively clearly visible, so that a magnetic field associated with CP2 stars can be assumed (see Fig. 6 for the part of the spectrum with the lines of interest and Fig. 7 for the full LAMOST spectrum of the star). Photometric data from VOSA resulted in an infrared-excess in the mid-infrared region. When we combine this with the finding that the spectrum also shows emission in the Hα line, we can conclude that this is also a Herbig Ae/Be object. This is another indication that the two object types are related.

thumbnail Fig. 6.

Blue-violet part of the rectified spectrum of the newly found CP candidate Gaia DR3 3368982075084757632. The spectral lines of interest are indicated. However the S/N of the spectrum is a slightly low, so that the exact type remains to be determined.

thumbnail Fig. 7.

Full spectrum of the newly found PMS CP candidate (see text and Fig. 6 as observed by LAMOST. The 5200 Å flux depression is shown in blue and the Hα emission is shown in red.

Combining the spectroscopic results with the results of the TESS light curves, we find that 32 of our 45 candidates are definitely or likely CP stars, resulting in a CP star of 71%.

9.5. Pre-maim-sequence chemically peculiar stars

Combining all the results from our analysis, which consist of spectral classification, SED fitting, and light curve analysis, we can determine how many of our stars are PMS-CP stars. Nine probable sources have at least one of these characteristics. Out of these, eight sources have a CP spectral type either in the literature, derived by us or both, and one lacks any CP spectral type. However, the light curve data for this star shows variability associated with ACV variables (Fig. B.32). The primary data (coordinates, parallaxes and spectral types)of our PMS CP stars are shown in Table 3 and the criteria for classifying the stars as pre-main sequence, in our case, IR-excess, are given in Table 4. The star Gaia DR3 368982075084757632, also shows emission in Hα, which is a sign of accretion onto the star and it thus indicates the PMS status of this source (Fig. 7).

Table 3.

Basic properties of our PMS CP candidates.

Table 4.

Filters in which the IR excess stars to show according to VOSA.

9.5.1. SHI261/Gaia DR3 121406905707934464

This star has already been classified as B8-9 Si He-wk by Potravnov et al. (2023b). The authors also analysed the evolutionary status of this star in detail. They concluded that it is still a PMS object that recently completed the accretion state in its evolution. They derived an IR excess starting in the region of the WISE W3 band. Our analysis based on spectra and SED fitting cenfirms those findings. Our manually determined spectral type is B9 III-IV Si. Additionally, the IR excess determined by VOSA starts around the same wavelengths as in Potravnov et al. (2023b), confirming the PMS status of the star.

9.5.2. R70/Gaia DR3 216648703146774016

The spectral type of this star was given as A0 SiSr in Renson & Manfroid (2009). Our analysis revealed a flux depression around 5200 Å in the Gaia BP/RP spectrum, strengthening the argument that it is a CP2 star. The IR excess of the star starts to show in the AKARI WIDE-S passband.

9.5.3. SHA20504/Gaia DR3 3019972890876467968

This star was classified as B6 VI Si by Shang et al. (2022) using machine learning on LAMOST DR8. Our spectral classification of B8 IV-V Si confirms the CP status. The assignment of a PMS-type star comes from the IR excess starting at the WISE W4 passband, as determined by VOSA.

9.5.4. Z583/Gaia DR3 3368982075084757632

This star has not been named a CP star in the literature. However, looking at the spectrum from LAMOST (Fig. 7), we can see the flux depression around 5200 Å, which, together with our derived spectral type B9 Ve SrSiEu, hints at the star being a CP candidate. However, as mentioned above, the SNR of the spectrum is poor, so we note that the spectral type is not final and needs a better spectrum to classify the peculiarity or even disprove it accurately. Some spectral lines of interest are denoted in Fig. 6. The PMS status is made clear by the Hα emission in the LAMOST spectra (see the red region in Fig. 7) and the IR excess that was determined from the SED fitting starting at the WISE W3 band.

9.5.5. R2175/Gaia DR3 3326717260430731648

This is another CP star from Renson & Manfroid (2009) who reported the spectral type as B6 He var. Its PMS status was determined using the IR excess in the SED starting in the WISE W3 band.

9.5.6. R5585/Gaia DR3 6244725050721030528

This star has been classified as B9 SiCrSr according to Renson & Manfroid (2009). The BP/RP spectrum also revealed a clear flux depression associated with magnetic stars, further confirming the CP nature of this star. Our analysis regarding the PMS status of the source shows IR excess at wavelengths beyond the WISE W3 band.

9.5.7. R5765/Gaia DR3 5966515967154648064

Although we failed to find a classification as a CP star in the literature (Renson & Manfroid 2009 determined it to be an A-type star), the positive Δa value marks it as a candidate for being a magnetic star. Unfortunately, we were unable to obtain a spectrum from the databases we searched that would have allowed a detailed classification. However, the light curve from TESS shows a variability that is commonly associated with ACV variables, which is another strong argument for the CP nature (Fig. B.32). We conclude that the star is still in the PMS stage based on the SED that shows IR excess starting at the WISE W2 band.

9.5.8. R7193/Gaia DR3 2244529022468154880

This star has also been classified as a CP star in the literature. Renson & Manfroid (2009) listed the stars as an A0 Si star, which is accompanied by a positive Δa value. The CP classification is therefore reliable. It is also a PMS star because we detected an IR excess starting at the WISE W1 band.

9.6. Non-chemically peculiar stars as members of open clusters

As described in Sect. 2, we did not exclude any objects from the various sources of CP stars. After the matching, we checked all objects to determine whether they belonged to their subgroup. For 24 objects from Renson & Manfroid (2009) we were unable to find any confirmation that they would be a CP star. They also have regular Δa values (Sect. 4).

9.7. Colour-magnitude diagrams

Using the cluster members from Hunt & Reffert (2023), we plotted a CMD for each of the 39 clusters containing our CP stars and candidates. In the plots, we included the isochrones from Bressan et al. (2012) shifted and adapted by ages, distances, and extinctions given in Hunt & Reffert (2023). The results are shown in Appendix A. One CP candidate has a very red colour index of BP − RP ≈ 3.1 (Fig. A.28). This star was classified as a CP1 star by Qin et al. (2019) with the spectral type kA3hA3mA7.

However, since some CMDs have relatively high reddening values, we cannot determine the evolutionary status from the diagram alone. The differential extinction across the clusters would have to be considered as well. However, we can get an idea of where our CP stars and candidates lie.

10. Conclusions

We studied 45 suspected PMS stars that may also be CP stars to determine which of them are real PMS stars and which are CP stars, or at least candidates, either labelled as such by previous authors or CP candidates that need closer investigation.

We find a CP fraction of 71% in our sample (including CP stars and CP candidates), of which nine stars are likely still in their PMS phase (Sect. 9.5). Based on our analysis of the available spectral types in the literature and those we determined, accompanied by photometric variability studies, we conclude that all of our PMS CP sources appear to belong to the subclass of magnetic CP stars.

This result can be interpreted as an argument that the magnetic fields found in these stars are an essential part of the early evolution of CP stars and would at least partially explain the origin of these subtypes. However, the origin and evolution of these magnetic fields remains to be discussed in detail.

However, we note that some of the spectral types are still somewhat ambiguous due to the quality of the data. More and better data are required to accurately determine the correct type of peculiarity, especially for four of our newly found CP candidates.

Acknowledgments

This work was supported by the grant GAČR 23-07605S and the European Regional Development Fund, project No. ITMS2014+: 313011W085 (MP). This work has made use of data from the European Space Agency (ESA) mission Gaia (https://www.cosmos.esa.int/gaia), processed by the Gaia Data Processing and Analysis Consortium (DPAC, https://www.cosmos.esa.int/web/gaia/dpac/consortium). Funding for the DPAC has been provided by national institutions, in particular, the institutions participating in the Gaia Multilateral Agreement. This research has made use of the SIMBAD database, operated at CDS, Strasbourg, France and of the WEBDA database, operated at the Department of Theoretical Physics and Astrophysics of the Masaryk University. This publication makes use of VOSA, developed under the Spanish Virtual Observatory (https://svo.cab.inta-csic.es) project funded by MCIN/AEI/10.13039/501100011033/ through grant PID2020-112949GB-I00. VOSA has been partially updated by using funding from the European Union’s Horizon 2020 Research and Innovation Programme, under Grant Agreement n°776403 (EXOPLANETS-A). We also thank the anonymous referee for their valuable feedback.

References

  1. Abt, H. A. 2009, AJ, 138, 28 [NASA ADS] [CrossRef] [Google Scholar]
  2. Adelman, S. J., Adelman, A. S., & Pintado, O. I. 2003, A&A, 397, 267 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  3. Anders, F., Khalatyan, A., Chiappini, C., et al. 2019, A&A, 628, A94 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  4. Anders, F., Khalatyan, A., Queiroz, A. B. A., et al. 2022, A&A, 658, A91 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  5. Bayo, A., Rodrigo, C., Barrado Y Navascués, D., et al. 2008, A&A, 492, 277 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  6. Bernhard, K., Hümmerich, S., Otero, S., & Paunzen, E. 2015a, A&A, 581, A138 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  7. Bernhard, K., Hümmerich, S., & Paunzen, E. 2015b, Astron. Nachr., 336, 981 [NASA ADS] [CrossRef] [Google Scholar]
  8. Bressan, A., Marigo, P., Girardi, L., et al. 2012, MNRAS, 427, 127 [NASA ADS] [CrossRef] [Google Scholar]
  9. Brittain, S. D., Kamp, I., Meeus, G., Oudmaijer, R. D., & Waters, L. B. F. M. 2023, Space Sci. Rev., 219, 7 [NASA ADS] [CrossRef] [Google Scholar]
  10. Bychkov, V. D., Bychkova, L. V., & Madej, J. 2021, A&A, 652, A31 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  11. Cantat-Gaudin, T., Anders, F., Castro-Ginard, A., et al. 2020, A&A, 640, A1 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  12. Cariddi, S., Azatyan, N. M., Kurfürst, P., et al. 2018, New Astron., 58, 1 [NASA ADS] [CrossRef] [Google Scholar]
  13. Castelli, F., & Kurucz, R. L. 2003, in Modelling of Stellar Atmospheres, eds. N. Piskunov, W. W. Weiss, & D. F. Gray, 210, A20 [Google Scholar]
  14. Catalano, F. A., & Renson, P. 1998, A&AS, 127, 421 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  15. Chen, P. S., Liu, J. Y., & Shan, H. G. 2017, AJ, 153, 218 [Google Scholar]
  16. Chen, X., Wang, S., Deng, L., et al. 2020, ApJS, 249, 18 [NASA ADS] [CrossRef] [Google Scholar]
  17. Choi, J., Dotter, A., Conroy, C., et al. 2016, ApJ, 823, 102 [Google Scholar]
  18. Chojnowski, S. D., Hubrig, S., Hasselquist, S., et al. 2020, MNRAS, 496, 832 [NASA ADS] [CrossRef] [Google Scholar]
  19. Cui, X.-Q., Zhao, Y.-H., Chu, Y.-Q., et al. 2012, Res. Astron. Astrophys., 12, 1197 [Google Scholar]
  20. Dekker, H., D’Odorico, S., Kaufer, A., Delabre, B., & Kotzlowski, H. 2000, in Optical and IR Telescope Instrumentation and Detectors, eds. M. Iye, & A. F. Moorwood, SPIE Conf. Ser., 4008, 534 [Google Scholar]
  21. Deutsch, A. J. 1947, ApJ, 105, 283 [Google Scholar]
  22. Dias, W. S., Monteiro, H., Moitinho, A., et al. 2021, MNRAS, 504, 356 [NASA ADS] [CrossRef] [Google Scholar]
  23. Dotter, A. 2016, ApJS, 222, 8 [Google Scholar]
  24. Feinstein, A. D., Montet, B. T., Foreman-Mackey, D., et al. 2019, PASP, 131, 094502a [NASA ADS] [CrossRef] [Google Scholar]
  25. Folsom, C. P., Bagnulo, S., Wade, G. A., et al. 2012, MNRAS, 422, 2072 [NASA ADS] [CrossRef] [Google Scholar]
  26. Fouesneau, M., Frémat, Y., Andrae, R., et al. 2023, A&A, 674, A28 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  27. Gaia Collaboration (Brown, A. G. A., et al.) 2021, A&A, 649, A1 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  28. Ghazaryan, S., Alecian, G., & Hakobyan, A. A. 2018, MNRAS, 480, 2953 [NASA ADS] [CrossRef] [Google Scholar]
  29. Ghazaryan, S., Alecian, G., & Hakobyan, A. A. 2019, MNRAS, 487, 5922 [NASA ADS] [CrossRef] [Google Scholar]
  30. Gray, R. O., & Corbally, C. J. 2014, AJ, 147, 80 [CrossRef] [Google Scholar]
  31. Gray, R. O., Corbally, C. J., De Cat, P., et al. 2016, AJ, 151, 13 [Google Scholar]
  32. Hümmerich, S., Mikulášek, Z., Paunzen, E., et al. 2018, A&A, 619, A98 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  33. Hümmerich, S., Paunzen, E., & Bernhard, K. 2020, A&A, 640, A40 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  34. Hunt, E. L., & Reffert, S. 2023, A&A, 673, A114 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  35. Khan, S. A., & Shulyak, D. V. 2007, A&A, 469, 1083 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  36. Kupka, F., Paunzen, E., & Maitzen, H. M. 2003, MNRAS, 341, 849 [NASA ADS] [CrossRef] [Google Scholar]
  37. Kurucz, R. L. 1993, VizieR Online Data Catalog: VI/39 [Google Scholar]
  38. Kyritsis, E., Maravelias, G., Zezas, A., et al. 2022, A&A, 657, A62 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  39. Lada, C. J. 2005, Prog. Theor. Phys. Suppl., 158, 1 [NASA ADS] [CrossRef] [Google Scholar]
  40. Li, C., Zhang, W. H., & Lin, J. M. 2019, Acta Astron. Sin., 60, 16 [Google Scholar]
  41. Lomb, N. R. 1976, Ap&SS, 39, 447 [Google Scholar]
  42. Majewski, S. R., Schiavon, R. P., Frinchaboy, P. M., et al. 2017, AJ, 154, 94 [NASA ADS] [CrossRef] [Google Scholar]
  43. Maury, A. C., & Pickering, E. C. 1897, Ann. Harvard College Obs., 28, 1 [Google Scholar]
  44. Mayor, M., Pepe, F., Queloz, D., et al. 2003, The Messenger, 114, 20 [NASA ADS] [Google Scholar]
  45. McInnes, L., Healy, J., & Astels, S. 2017, J. Open Source Software, 2, 205 [NASA ADS] [CrossRef] [Google Scholar]
  46. Meingast, S., Handler, G., & Shobbrook, R. R. 2013, A&A, 559, A108 [EDP Sciences] [Google Scholar]
  47. Michaud, G., & Charland, Y. 1986, ApJ, 311, 326 [Google Scholar]
  48. Moss, D. 2003, A&A, 403, 693 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  49. Netopil, M. 2013, PhD Thesis, University of Vienna, Austria [Google Scholar]
  50. Netopil, M., Paunzen, E., Maitzen, H. M., et al. 2007, A&A, 462, 591 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  51. Netopil, M., Fossati, L., Paunzen, E., et al. 2014, MNRAS, 442, 3761 [NASA ADS] [CrossRef] [Google Scholar]
  52. Netopil, M., Paunzen, E., & Carraro, G. 2015, A&A, 582, A19 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  53. Netopil, M., Paunzen, E., Hümmerich, S., & Bernhard, K. 2017, MNRAS, 468, 2745 [NASA ADS] [CrossRef] [Google Scholar]
  54. Nichols, J. S., Henden, A. A., Huenemoerder, D. P., et al. 2010, ApJS, 188, 473 [Google Scholar]
  55. Oelkers, R. J., Rodriguez, J. E., Stassun, K. G., et al. 2018, AJ, 155, 39 [Google Scholar]
  56. Palla, F., & Stahler, S. W. 1990, ApJ, 360, L47 [Google Scholar]
  57. Paunzen, E. 2024, A&A, 683, L7 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  58. Paunzen, E., & Prišegen, M. 2022, A&A, 667, L10 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  59. Paunzen, E., Stütz, C., & Maitzen, H. M. 2005, A&A, 441, 631 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  60. Paunzen, E., Iliev, I. K., Fossati, L., Heiter, U., & Weiss, W. W. 2014a, A&A, 567, A67 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  61. Paunzen, E., Netopil, M., Maitzen, H. M., et al. 2014b, A&A, 564, A42 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  62. Paunzen, E., Hümmerich, S., & Bernhard, K. 2021a, A&A, 645, A34 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  63. Paunzen, E., Supíková, J., Bernhard, K., Hümmerich, S., & Prišegen, M. 2021b, MNRAS, 504, 3758 [NASA ADS] [CrossRef] [Google Scholar]
  64. Paxton, B., Bildsten, L., Dotter, A., et al. 2011, ApJS, 192, 3 [Google Scholar]
  65. Paxton, B., Cantiello, M., Arras, P., et al. 2013, ApJS, 208, 4 [Google Scholar]
  66. Paxton, B., Marchant, P., Schwab, J., et al. 2015, ApJS, 220, 15 [Google Scholar]
  67. Pedersen, H., & Thomsen, B. 1977, A&AS, 30, 11 [NASA ADS] [Google Scholar]
  68. Potravnov, I., Mashonkina, L., & Ryabchikova, T. 2023a, MNRAS, 520, 1296 [NASA ADS] [CrossRef] [Google Scholar]
  69. Potravnov, I., Ryabchikova, T., Artemenko, S., & Eselevich, M. 2023b, Universe, 9, 210 [NASA ADS] [CrossRef] [Google Scholar]
  70. Preston, G. W. 1974, ARA&A, 12, 257 [Google Scholar]
  71. Qin, L., Luo, A. L., Hou, W., et al. 2019, ApJS, 242, 13 [NASA ADS] [CrossRef] [Google Scholar]
  72. Renson, P., & Manfroid, J. 2009, A&A, 498, 961 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  73. Ricker, G. R., Winn, J. N., Vanderspek, R., et al. 2015, J. Astron. Telesc. Instrum. Syst., 1, 014003 [Google Scholar]
  74. Romanyuk, I. I., Semenko, E. A., Yakunin, I. A., & Kudryavtsev, D. O. 2013, Astrophys. Bull., 68, 300 [Google Scholar]
  75. Scargle, J. D. 1982, ApJ, 263, 835 [Google Scholar]
  76. Schleicher, D. R. G., Hidalgo, J. P., & Galli, D. 2023, A&A, 678, A204 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  77. Shang, L.-H., Luo, A. L., Wang, L., et al. 2022, ApJS, 259, 63 [NASA ADS] [CrossRef] [Google Scholar]
  78. Shi, F., Zhang, H., Fu, J., Kurtz, D., & Xiang, M. 2023, ApJ, 943, 147 [NASA ADS] [CrossRef] [Google Scholar]
  79. Shultz, M. E., Owocki, S. P., ud-Doula, A., et al. 2022, MNRAS, 513, 1429 [NASA ADS] [CrossRef] [Google Scholar]
  80. Smith, K. C. 1996, Ap&SS, 237, 77 [NASA ADS] [CrossRef] [Google Scholar]
  81. Stahler, S. W. 1983, ApJ, 274, 822 [Google Scholar]
  82. Stassun, K. G., Oelkers, R. J., Paegert, M., et al. 2019, AJ, 158, 138 [Google Scholar]
  83. Stibbs, D. W. N. 1950, MNRAS, 110, 395 [Google Scholar]
  84. Théado, S., Vauclair, S., & Cunha, M. S. 2005, A&A, 443, 627 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  85. Trifonov, T., Tal-Or, L., Zechmeister, M., et al. 2020, A&A, 636, A74 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  86. Venn, K. A., & Lambert, D. L. 1990, ApJ, 363, 234 [Google Scholar]
  87. Vernet, J., Dekker, H., D’Odorico, S., et al. 2011, A&A, 536, A105 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  88. Vines, J. I., & Jenkins, J. S. 2022, MNRAS, 513, 2719 [NASA ADS] [CrossRef] [Google Scholar]
  89. Zhang, Y.-J., Hou, W., Luo, A. L., et al. 2022, ApJS, 259, 38 [NASA ADS] [CrossRef] [Google Scholar]
  90. Zhang, X., Green, G. M., & Rix, H.-W. 2023, MNRAS, 524, 1855 [NASA ADS] [CrossRef] [Google Scholar]
  91. Zhao, G., Zhao, Y.-H., Chu, Y.-Q., Jing, Y.-P., & Deng, L.-C. 2012, Res. Astron. Astrophys., 12, 723 [NASA ADS] [CrossRef] [Google Scholar]

Appendix A: Colour-magnitude diagrams of the host clusters

thumbnail Fig. A.1.

Colour-magnitude diagram of the cluster Biurakan 2. The black dots are members of the cluster according to Hunt & Reffert (2023), and the red dots are our CP star candidates. The PARSEC isochrone (Bressan et al. 2012) was computed using the AV and log t values from the catalogue of Hunt & Reffert (2023) and using solar metallicity (Z = 0.0152). To "fit" the cluster, they were shifted according to the distance of the cluster, which was also taken from Hunt & Reffert (2023).

thumbnail Fig. A.2.

Same as Fig. A.1, but for Collinder 69.

thumbnail Fig. A.3.

Same as Fig. A.1, but for Collinder 106.

thumbnail Fig. A.4.

Same as Fig. A.1, but for Collinder 419.

thumbnail Fig. A.5.

Same as Fig. A.1, but for CWNU 1083.

thumbnail Fig. A.6.

Same as Fig. A.1, but for FSR 0219.

thumbnail Fig. A.7.

Same as Fig. A.1, but for Gulliver 2.

thumbnail Fig. A.8.

Same as Fig. A.1, but for HSC 899.

thumbnail Fig. A.9.

Same as Fig. A.1, but for HSC 1262.

thumbnail Fig. A.10.

Same as Fig. A.1, but for HSC 2468.

thumbnail Fig. A.11.

Same as Fig. A.1, but for HSC 2931.

thumbnail Fig. A.12.

Same as Fig. A.1, but for IC 348.

thumbnail Fig. A.13.

Same as Fig. A.1, but for Majaess 99.

thumbnail Fig. A.14.

Same as Fig. A.1, but for Mon OB1-D.

thumbnail Fig. A.15.

Same as Fig. A.1, but for NGC 1333.

thumbnail Fig. A.16.

Same as Fig. A.1, but for NGC 1502.

thumbnail Fig. A.17.

Same as Fig. A.1, but for NGC 1980.

thumbnail Fig. A.18.

Same as Fig. A.1, but for NGC 2169.

thumbnail Fig. A.19.

Same as Fig. A.1, but for NGC 2232.

thumbnail Fig. A.20.

Same as Fig. A.1, but for NGC 2244.

thumbnail Fig. A.21.

Same as Fig. A.1, but for NGC 2264.

thumbnail Fig. A.22.

Same as Fig. A.1, but for NGC 6178.

thumbnail Fig. A.23.

Same as Fig. A.1, but for NGC 6193.

thumbnail Fig. A.24.

Same as Fig. A.1, but for NGC 6231.

thumbnail Fig. A.25.

Same as Fig. A.1, but for NGC 6530.

thumbnail Fig. A.26.

Same as Fig. A.1, but for NGC 7160.

thumbnail Fig. A.27.

Same as Fig. A.1, but for OC 0185.

thumbnail Fig. A.28.

Same as Fig. A.1, but for OC 0322.

thumbnail Fig. A.29.

Same as Fig. A.1, but for OC 0357.

thumbnail Fig. A.30.

Same as Fig. A.1, but for OCSN 61.

thumbnail Fig. A.31.

Same as Fig. A.1, but for OCSN 96.

thumbnail Fig. A.32.

Same as Fig. A.1, but for Sigma Orionis.

thumbnail Fig. A.33.

Same as Fig. A.1, but for Theia 11.

thumbnail Fig. A.34.

Same as Fig. A.1, but for UBC 438.

thumbnail Fig. A.35.

Same as Fig. A.1, but for UPK 39.

thumbnail Fig. A.36.

Same as Fig. A.1, but for UPK 160.

thumbnail Fig. A.37.

Same as Fig. A.1, but for UPK 169.

thumbnail Fig. A.38.

Same as Fig. A.1, but for UPK 640.

thumbnail Fig. A.39.

Same as Fig. A.1, but for vdBergh 92.

Appendix B: TESS light curves

thumbnail Fig. B.1.

Known Pre-MS CP star without discernable variability in the TESS light curve. In the literature, it is an ACV variable with a period of about 123d.

thumbnail Fig. B.2.

Mixture of two pulsational variability types, GDOR and DSCT in the light curve. The main pulsation with a period of 1.509d comes from the GDOr part.

thumbnail Fig. B.3.

Clear CP star. Based on the light curve and spectrum, we conclude that the variability type is SXARI. The star is folded at half the period of 0.948d.

thumbnail Fig. B.4.

Irregular variability. However, it is possible that some pulsation or rotation feature is present. The low amplitude of the variation prevents us from concluding.

thumbnail Fig. B.5.

Clearly rotating variable star, probably of the type ACV.

thumbnail Fig. B.6.

Possible binary star.

thumbnail Fig. B.7.

Another ACV variable. A hint of a double wave is visible.

thumbnail Fig. B.8.

Slowly pulsating B (SPB) star.

thumbnail Fig. B.9.

ACV variable

thumbnail Fig. B.10.

Likely a pulsating star of SPB type.

thumbnail Fig. B.11.

Rotating variable star.

thumbnail Fig. B.12.

ACV variable.

thumbnail Fig. B.13.

Star belonging to the group of SXARI stars.

thumbnail Fig. B.14.

Basic rotation feature with slightly asymmetric sine waves. An ACV variable.

thumbnail Fig. B.15.

Double wave of this ACV variable, folded at half the period.

thumbnail Fig. B.16.

Another SPB star.

thumbnail Fig. B.17.

TESS light curve of the known ACV star V682 Mon.

thumbnail Fig. B.18.

Eclipsing binary star (possibly). However the amplitude of the variability is pretty low.

thumbnail Fig. B.19.

Binary star. The signal likely comes from the close brighter eclipsing variable V649 Mon.

thumbnail Fig. B.20.

Another ACV variable.

thumbnail Fig. B.21.

Irregular variable without real variability from the star itself. Likely instrumental features are visible.

thumbnail Fig. B.22.

Maybe a rotating variable. The signal is not clear enough to conclude.

thumbnail Fig. B.23.

Irregular variability, likely low-amplitude instrumental variations.

thumbnail Fig. B.24.

Another SXARI variable.

thumbnail Fig. B.25.

Multiperiodic or semiregular variable with a possible rotation feature (P = 3.018d) and some superimposed pulsational properties.

thumbnail Fig. B.26.

A sinusoidal rotation feature. The amplitude variation comes from observations in different sectors.

thumbnail Fig. B.27.

Possibly a rotating star.

thumbnail Fig. B.28.

Source with mostly instrumental artefacts that cause a slight variation.

thumbnail Fig. B.29.

Probable rotation superimposed by pulsations, probably a ROT+GDOR hybrid.

thumbnail Fig. B.30.

Rotating star with amplitude variations possibly due to chemical spots on the surface. Thus possibly an SXARI variable.

thumbnail Fig. B.31.

Multiperiodic variable with a basic rotation signal at a period of 2.136d.

thumbnail Fig. B.32.

ACV variable previously classified as an SPB.

thumbnail Fig. B.33.

Star without visible variability from the light curve we obtained.

Table B.1.

Variability of our sample stars based on the TESS light curves. Entries with a dash mean that no light curve was available via our method.

Appendix C: Spectral types from the literature

Table C.1.

Spectral types of our candidates from the literature

All Tables

Table 1.

Spectral types determined by MKCLASS from the LAMOST spectra.

In the text
Table 2.

Same as Table 1 but for the seven stars with spectra available from the ESO archive.

In the text
Table 3.

Basic properties of our PMS CP candidates.

In the text
Table 4.

Filters in which the IR excess stars to show according to VOSA.

In the text
Table B.1.

Variability of our sample stars based on the TESS light curves. Entries with a dash mean that no light curve was available via our method.

In the text
Table C.1.

Spectral types of our candidates from the literature

In the text

All Figures

thumbnail Fig. 1.

Sky distribution of the final 45 targets we used for the further analysis in this work.
In the text
thumbnail Fig. 2.

Spectrum of the B8 IV Si (libr18) star Gaia DR3 121406905707934464/LAMOST J032919.94+312457.0. The Si II lines are shown.
In the text
thumbnail Fig. 3.

Comparison of the extinction values given by Hunt & Reffert (2023) and those estimated by astroARIADNE.
In the text
thumbnail Fig. 4.

Spectral energy distribution for the star R2175 (Gaia DR3 3326717260430731648) as measured by VOSA (black dots). The red spectrum is a model from Castelli (2003) with the parameters Teff = 13 000 K, log g = 4.0 and [Fe/H]= − 0.5 dex. The vertical green line denotes the effective wavelength of the WISE W2 band from the point at which the measured SED deviates from the model spectrum, and the IR excess starts to show.
In the text
thumbnail Fig. 5.

Same as Fig. 4 but here we show the output plot generated by astroARIADNE. The synthetic photometry is shown via the cyan points; the purple diamonds show the fit points. The synthetic spectrum is a spectrum with the parameters Teff = 9500 K, log g = 3.5 and [Fe/H]= − 0.1 dex taken from Kurucz (1993). This method probes a smaller wavelength range so that no IR excess can be seen here.
In the text
thumbnail Fig. 6.

Blue-violet part of the rectified spectrum of the newly found CP candidate Gaia DR3 3368982075084757632. The spectral lines of interest are indicated. However the S/N of the spectrum is a slightly low, so that the exact type remains to be determined.
In the text
thumbnail Fig. 7.

Full spectrum of the newly found PMS CP candidate (see text and Fig. 6 as observed by LAMOST. The 5200 Å flux depression is shown in blue and the Hα emission is shown in red.
In the text
thumbnail Fig. A.1.

Colour-magnitude diagram of the cluster Biurakan 2. The black dots are members of the cluster according to Hunt & Reffert (2023), and the red dots are our CP star candidates. The PARSEC isochrone (Bressan et al. 2012) was computed using the AV and log t values from the catalogue of Hunt & Reffert (2023) and using solar metallicity (Z = 0.0152). To "fit" the cluster, they were shifted according to the distance of the cluster, which was also taken from Hunt & Reffert (2023).
In the text
thumbnail Fig. A.2.

Same as Fig. A.1, but for Collinder 69.
In the text
thumbnail Fig. A.3.

Same as Fig. A.1, but for Collinder 106.
In the text
thumbnail Fig. A.4.

Same as Fig. A.1, but for Collinder 419.
In the text
thumbnail Fig. A.5.

Same as Fig. A.1, but for CWNU 1083.
In the text
thumbnail Fig. A.6.

Same as Fig. A.1, but for FSR 0219.
In the text
thumbnail Fig. A.7.

Same as Fig. A.1, but for Gulliver 2.
In the text
thumbnail Fig. A.8.

Same as Fig. A.1, but for HSC 899.
In the text
thumbnail Fig. A.9.

Same as Fig. A.1, but for HSC 1262.
In the text
thumbnail Fig. A.10.

Same as Fig. A.1, but for HSC 2468.
In the text
thumbnail Fig. A.11.

Same as Fig. A.1, but for HSC 2931.
In the text
thumbnail Fig. A.12.

Same as Fig. A.1, but for IC 348.
In the text
thumbnail Fig. A.13.

Same as Fig. A.1, but for Majaess 99.
In the text
thumbnail Fig. A.14.

Same as Fig. A.1, but for Mon OB1-D.
In the text
thumbnail Fig. A.15.

Same as Fig. A.1, but for NGC 1333.
In the text
thumbnail Fig. A.16.

Same as Fig. A.1, but for NGC 1502.
In the text
thumbnail Fig. A.17.

Same as Fig. A.1, but for NGC 1980.
In the text
thumbnail Fig. A.18.

Same as Fig. A.1, but for NGC 2169.
In the text
thumbnail Fig. A.19.

Same as Fig. A.1, but for NGC 2232.
In the text
thumbnail Fig. A.20.

Same as Fig. A.1, but for NGC 2244.
In the text
thumbnail Fig. A.21.

Same as Fig. A.1, but for NGC 2264.
In the text
thumbnail Fig. A.22.

Same as Fig. A.1, but for NGC 6178.
In the text
thumbnail Fig. A.23.

Same as Fig. A.1, but for NGC 6193.
In the text
thumbnail Fig. A.24.

Same as Fig. A.1, but for NGC 6231.
In the text
thumbnail Fig. A.25.

Same as Fig. A.1, but for NGC 6530.
In the text
thumbnail Fig. A.26.

Same as Fig. A.1, but for NGC 7160.
In the text
thumbnail Fig. A.27.

Same as Fig. A.1, but for OC 0185.
In the text
thumbnail Fig. A.28.

Same as Fig. A.1, but for OC 0322.
In the text
thumbnail Fig. A.29.

Same as Fig. A.1, but for OC 0357.
In the text
thumbnail Fig. A.30.

Same as Fig. A.1, but for OCSN 61.
In the text
thumbnail Fig. A.31.

Same as Fig. A.1, but for OCSN 96.
In the text
thumbnail Fig. A.32.

Same as Fig. A.1, but for Sigma Orionis.
In the text
thumbnail Fig. A.33.

Same as Fig. A.1, but for Theia 11.
In the text
thumbnail Fig. A.34.

Same as Fig. A.1, but for UBC 438.
In the text
thumbnail Fig. A.35.

Same as Fig. A.1, but for UPK 39.
In the text
thumbnail Fig. A.36.

Same as Fig. A.1, but for UPK 160.
In the text
thumbnail Fig. A.37.

Same as Fig. A.1, but for UPK 169.
In the text
thumbnail Fig. A.38.

Same as Fig. A.1, but for UPK 640.
In the text
thumbnail Fig. A.39.

Same as Fig. A.1, but for vdBergh 92.
In the text
thumbnail Fig. B.1.

Known Pre-MS CP star without discernable variability in the TESS light curve. In the literature, it is an ACV variable with a period of about 123d.
In the text
thumbnail Fig. B.2.

Mixture of two pulsational variability types, GDOR and DSCT in the light curve. The main pulsation with a period of 1.509d comes from the GDOr part.
In the text
thumbnail Fig. B.3.

Clear CP star. Based on the light curve and spectrum, we conclude that the variability type is SXARI. The star is folded at half the period of 0.948d.
In the text
thumbnail Fig. B.4.

Irregular variability. However, it is possible that some pulsation or rotation feature is present. The low amplitude of the variation prevents us from concluding.
In the text
thumbnail Fig. B.5.

Clearly rotating variable star, probably of the type ACV.
In the text
thumbnail Fig. B.6.

Possible binary star.
In the text
thumbnail Fig. B.7.

Another ACV variable. A hint of a double wave is visible.
In the text
thumbnail Fig. B.8.

Slowly pulsating B (SPB) star.
In the text
thumbnail Fig. B.9.

ACV variable
In the text
thumbnail Fig. B.10.

Likely a pulsating star of SPB type.
In the text
thumbnail Fig. B.11.

Rotating variable star.
In the text
thumbnail Fig. B.12.

ACV variable.
In the text
thumbnail Fig. B.13.

Star belonging to the group of SXARI stars.
In the text
thumbnail Fig. B.14.

Basic rotation feature with slightly asymmetric sine waves. An ACV variable.
In the text
thumbnail Fig. B.15.

Double wave of this ACV variable, folded at half the period.
In the text
thumbnail Fig. B.16.

Another SPB star.
In the text
thumbnail Fig. B.17.

TESS light curve of the known ACV star V682 Mon.
In the text
thumbnail Fig. B.18.

Eclipsing binary star (possibly). However the amplitude of the variability is pretty low.
In the text
thumbnail Fig. B.19.

Binary star. The signal likely comes from the close brighter eclipsing variable V649 Mon.
In the text
thumbnail Fig. B.20.

Another ACV variable.
In the text
thumbnail Fig. B.21.

Irregular variable without real variability from the star itself. Likely instrumental features are visible.
In the text
thumbnail Fig. B.22.

Maybe a rotating variable. The signal is not clear enough to conclude.
In the text
thumbnail Fig. B.23.

Irregular variability, likely low-amplitude instrumental variations.
In the text
thumbnail Fig. B.24.

Another SXARI variable.
In the text
thumbnail Fig. B.25.

Multiperiodic or semiregular variable with a possible rotation feature (P = 3.018d) and some superimposed pulsational properties.
In the text
thumbnail Fig. B.26.

A sinusoidal rotation feature. The amplitude variation comes from observations in different sectors.
In the text
thumbnail Fig. B.27.

Possibly a rotating star.
In the text
thumbnail Fig. B.28.

Source with mostly instrumental artefacts that cause a slight variation.
In the text
thumbnail Fig. B.29.

Probable rotation superimposed by pulsations, probably a ROT+GDOR hybrid.
In the text
thumbnail Fig. B.30.

Rotating star with amplitude variations possibly due to chemical spots on the surface. Thus possibly an SXARI variable.
In the text
thumbnail Fig. B.31.

Multiperiodic variable with a basic rotation signal at a period of 2.136d.
In the text
thumbnail Fig. B.32.

ACV variable previously classified as an SPB.
In the text
thumbnail Fig. B.33.

Star without visible variability from the light curve we obtained.
In the text

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