Related papers: SteParSyn: A Bayesian code to infer stellar atmosp…
We present stellar parameter estimates for 939,457 southern FGK stars that are candidate targets for the TESS mission. Using a data-driven method similar to the CANNON, we build a model of stellar colours as a function of stellar…
We present a method for the determination of [alpha/Fe] ratios from low-resolution (R = 2000) SDSS/SEGUE stellar spectra. By means of a star-by-star comparison with degraded spectra from the ELODIE spectral library and with a set of…
One of the greatest difficulties in astrophysics is the determination of the fundamental stellar parameters (FSPs), one of which is the initial mass fraction of helium (Y). However, given that Y can be measured spectroscopically in only a…
In search for a connection between the formation of stars and the formation of planets, a new semi-automatic spectral analysis method using \textsf{iSpec} was developed for the TIGRE telescope installed in Guanajuato, Mexico. TIGRE is a…
One of the biggest problems we can encounter while dealing with the limb-darkening coefficients for stellar atmospheric models with spherical symmetry is the difficulty of adjusting both the limb and the central parts simultaneously. In…
We developed a code that estimates distances to stars using measured spectroscopic and photometric quantities. We employ a Bayesian approach to build the probability distribution function over stellar evolutionary models given these data,…
Aims: Obtaining accurate derivations of stellar atmospheric parameters is crucial in the fields of stellar and exoplanet characterization. We present the upgraded version of our computational tool ODUSSEAS with a new reference scale applied…
We present a simple, physically-motivated model to interpret consistently the emission from galaxies at ultraviolet, optical and infrared wavelengths. We combine this model with a Bayesian method to obtain robust statistical constraints on…
A new generative technique is presented in this paper that uses Deep Learning to reconstruct stellar spectra based on a set of stellar parameters. Two different Neural Networks were trained allowing the generation of new spectra. First, an…
This work investigates the spectrum parameterization problem using deep neural networks (DNNs). The proposed scheme consists of the following procedures: first, the configuration of a DNN is initialized using a series of autoencoder neural…
(Abridged) We present the first APOKASC catalog of spectroscopic and asteroseismic data for 415 dwarfs and subgiants. Asteroseismic data have been obtained by Kepler in short cadence. The spectroscopic parameters are based on spectra taken…
The currently operating space missions, as well as those that will be launched in the near future, (will) deliver high-quality data for millions of stellar objects. Since the majority of stellar astrophysical applications still (at least…
We present an homogeneous set of stellar atmospheric parameters (Teff, log g, [Fe/H]) for a sample of about 700 field and cluster stars which constitute a new stellar library in the near-infrared developed for stellar population synthesis…
The accuracy of the estimated stellar atmospheric parameter decreases evidently with the decreasing of spectral signal-to-noise ratio (SNR) and there are a huge amount of this kind observations, especially in case of SNR$<$30. Therefore, it…
We validate the performance and accuracy of the current SEGUE (Sloan Extension for Galactic Understanding and Exploration) Stellar Parameter Pipeline (SSPP), which determines stellar atmospheric parameters (effective temperature, surface…
Accurate and homogeneous atmospheric parameters (Teff, log (g), Vt, [Fe/H]) are derived for 74 FGK non-variable supergiants from high-resolution, high signal-to-noise ratio, echelle spectra. Extremely high precision for the inferred…
We describe advances on a method designed to derive accurate parameters of M dwarfs. Our analysis consists in comparing high-resolution infrared spectra acquired with the near-infrared spectro-polarimeter SPIRou to synthetic spectra…
Data-driven models, which apply machine learning to infer physical properties from large quantities of data, have become increasingly important for extracting stellar properties from spectra. In general, these methods have been applied to…
Understanding the formation and evolution of our Galaxy requires accurate distances, ages and chemistry for large populations of field stars. Here we present several updates to our spectro-photometric distance code, that can now also be…
The goal of AIMS (Asteroseismic Inference on a Massive Scale) is to estimate stellar parameters and credible intervals/error bars in a Bayesian manner from a set of asteroseismic frequency data and so-called classical constraints. To…