Issue |
A&A
Volume 665, September 2022
|
|
---|---|---|
Article Number | A36 | |
Number of page(s) | 17 | |
Section | Catalogs and data | |
DOI | https://doi.org/10.1051/0004-6361/202243839 | |
Published online | 08 September 2022 |
Analysis of high-resolution FEROS spectroscopy for a sample of variable B-type stars assembled from TESS photometry★,★★
1
Institute of Astronomy,
KU Leuven, Celestijnenlaan 200D,
3001
Leuven, Belgium
e-mail: sarah.gebruers@kuleuven.be
2
Max Planck Institute for Astronomy,
Königstuhl 17,
69117
Heidelberg, Germany
3
Koninklijke Sterrenwacht van België,
Ringlaan 3,
1180
Brussel, Belgium
4
The Department of Astronomy and Center of Cosmology and AstroParticle Physics, The Ohio State University,
Columbus, OH
43210, USA
5
Department of Astrophysics, IMAPP, Radboud University Nijmegen,
PO Box 9010,
6500 GL
Nijmegen, The Netherlands
Received:
22
April
2022
Accepted:
19
June
2022
Context. Spectroscopic data are necessary to break degeneracies in the asteroseismic modelling of the interior structure in high- and intermediate-mass stars. With the TESS mission, the number of bright intermediate-mass B-type stars with long photometric light curves that are suitable for detailed asteroseismic studies has increased substantially compared to the pre-TESS era.
Aims. We derive precise photospheric stellar parameters for a sample of 166 B-type stars with TESS light curves through a homogeneous spectroscopic analysis. The variability types of these sample stars are also classified based on all currently available TESS sectors, and they are ultimately prioritised according to their astrophysical potential.
Methods. We obtained high-resolution spectra for all 166 targets with the FEROS spectrograph in the context of a large program. The spectra were reduced with the CERES pipeline, which we adapted to improve the quality of the reduced spectra. These spectra were subsequently analysed with ZETA-PAYNE, a machine-learning-based spectrum analysis algorithm, to infer precise stellar labels for all stars in the sample. Furthermore, the least-squares deconvolution (LSD) method was employed to investigate spectral line profile variability (LPV) and isolate binary systems from presumably single stars.
Results. The LSD profile analysis identified 26 spectroscopic double-lined binaries; the remainder of the sample are 42 supergiants in the Large Magellanic Cloud galaxy and 98 Galactic stars, both with and without apparent LPV. For the Galactic single stars and single-lined spectroscopic binaries, we determine their five main surface parameters: effective temperature (Teff), surface gravity (log g), global metallicity ([M/H]), projected rotational velocity (v sin i), and microturbulent velocity (ξ) with average formal precisions of 70 K, 0.03 dex, 0.07 dex, 8 km s−1, and 0.7 km s−1, respectively. The average internal uncertainties we find for FEROS spectra with our spectrum analysis method are 430 K(Teff), 0.12 dex (log g), 0.13 dex ([M/H]), 12kms−1 (v sin i), and 2 kms−1 (ξ).
Conclusions. We find spectroscopic evidence that 8 of the 98 galactic single or SB1 variables are fast-rotating gravity-mode pulsators occurring in between the slowly pulsating B (SPB) stars and δ Scuti instability strips. The g-mode frequencies of these pulsators are shifted to relatively high frequency values due to their rotation, and their apparently too low Teff relative to the SPB instability region can in most cases be explained by the gravity darkening effect. We also discover 13 new HgMn stars in the Galactic sample of which only one is found in a spectroscopic binary, resulting in a biased and therefore unreliable low binary rate of only 8%.
Key words: stars: fundamental parameters / stars: variables: general / stars: oscillations / asteroseismology / techniques: spectroscopic
Tables A.1 and A.2 are only available at the CDS via anonymous ftp to cdsarc.u-strasbg.fr (130.79.128.5) or via http://cdsarc.u-strasbg.fr/viz-bin/cat/J/A+A/665/A36
© S. Gebruers et al. 2022
Open 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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