Related papers: Predicting Stellar Parameters of Massive Stars fro…
This paper reports on the application of the supervised machine-learning algorithm to the stellar effective temperature regression for the second $Gaia$ data release, based on the combination of the stars in four spectroscopic surveys:…
We introduce a new machine learning based technique to detect exoplanets using the transit method. Machine learning and deep learning techniques have proven to be broadly applicable in various scientific research areas. We aim to exploit…
The properties of exoplanet host stars are traditionally characterized through a detailed forward-modeling analysis of high-resolution spectra. However, many exoplanet radial velocity surveys employ iodine-cell-calibrated spectrographs,…
We used a convolutional neural network to infer stellar rotation periods from a set of synthetic light curves simulated with realistic spot evolution patterns. We convolved these simulated light curves with real TESS light curves containing…
Astrophysical observations, theoretical models, and terrestrial experiments probe different regions of neutron star (NS) interior. Therefore, it is essential to consistently combine the information from these sources. This analysis requires…
Accurate limb-darkening models are needed for accurate characterisation of eclipsing binary stars and transiting exoplanets from the analysis of their light curves. The limb-darkening observed in solar-type stars from the analysis of light…
We present a new machine learning model for estimating photometric redshifts with improved accuracy for galaxies in Pan-STARRS1 data release 1. Depending on the estimation range of redshifts, this model based on neural networks can handle…
The growth of sky surveys and the large amount of stellar spectra in the current databases, has generated the necessity of developing new methods to estimate atmospheric parameters, a fundamental task on stellar research. In this work we…
The interiors of neutron stars reach densities and temperatures beyond the limits of terrestrial experiments, providing vital laboratories for probing nuclear physics. While the star's interior is not directly observable, its pressure and…
We pose the question of how much information on the atmospheric parameters of late-type stars can be retrieved purely from colors using standard photometric systems. We carried out numerical experiments using stellar fluxes from model…
Asteroseismology is the study of resonant oscillations of stars to infer their internal structure and dynamics. It is also a powerful tool for precisely determining stellar parameters such as mass, radius, surface gravity, and age. The…
Identifying stars belonging to different classes is vital in order to build up statistical samples of different phases and pathways of stellar evolution. In the era of surveys covering billions of stars, an automated method of identifying…
Stellar rotation is a fundamental parameter governing a star's magnetic activity and evolution. The Transiting Exoplanet Survey Satellite (TESS) provides high-precision photometric data ideal for measuring rotation periods via brightness…
We provide an observational view of evolutionary models in the Hertzsprung--Russell diagram, on the main sequence. For that we computed evolutionary models with the code STAREVOL for 15 < M/Msun < 100. We subsequently calculated atmosphere…
We describe a technique for deriving effective temperatures, surface gravities, rotation velocities, and radial velocities from high resolution near-IR spectra. The technique matches the observed near-IR spectra to spectra synthesized from…
Variable stars with well-calibrated period-luminosity relationships provide accurate distance measurements to nearby galaxies and are therefore a vital tool for cosmology and astrophysics. While these measurements typically rely on samples…
Since the end of 2018, the Transiting Exoplanet Survey Satellite (TESS) has provided stellar photometry to the astronomical community. We have used TESS data to study rotational modulation in the light curves of a sample of chemically…
Machine Learning is an efficient method for analyzing and interpreting the increasing amount of astronomical data that is available. In this study, we show, a pedagogical approach that should benefit anyone willing to experiment with Deep…
In order to better determine the physical properties of hot, massive stars as a function of metallicity, we obtained very high SNR optical spectra of 26 O and early B stars in the Magellanic Clouds. These allow accurate modeling even in…
We describe a self-consistent spectrum analysis technique employing non-LTE line formation, which allows precise atmospheric parameters of massive stars to be derived: 1sigma-uncertainties as low as ~1% in effective temperature and…