Related papers: StePar: an automatic code to infer stellar atmosph…
The ~ 200,000 stars observed by the Kepler mission have provided unprecedented constraints across astrophysics. With the advent of modern spectroscopic and photometric surveys, new limits in stellar characterizations are within reach. In…
In this paper, we developed a spectral emulator based on the Mapping Nearby Galaxies at Apache Point Observatory Stellar Library (MaStar) and a grouping optimization strategy to estimate effective temperature (T_eff), surface gravity (log…
Stellar atmosphere modelling predicts the luminosity and temperature of a star, together with parameters such as the effective gravity and the metallicity, by reproducing the observed spectral energy distribution. Most observational data…
We present MINESweeper, a tool to measure stellar parameters by jointly fitting observed spectra and broadband photometry to model isochrones and spectral libraries. This approach enables the measurement of spectrophotometric distances, in…
Accurately measuring stellar parameters is a key goal to increase our understanding of the observable universe. However, current methods are limited by many factors, in particular, the biases and physical assumptions that are the basis for…
The advent of large-scale stellar spectroscopic surveys naturally leads to the implementation of machine learning techniques to isolate, for example, small sub-samples of potentially interesting stars from the full data set. A recent…
Asteroseismic observations are crucial to constrain stellar models with precision. Bayesian Estimation of STellar Parameters (BESTP) is a tool that utilizes Bayesian statistics and nested sampling Monte Carlo algorithm to search for the…
In this paper we describe the FIT\textit{spec} code, a data mining tool for the automatic fitting of synthetic stellar spectra. The program uses a database of 27\,000 {\sc cmfgen} models of stellar atmospheres arranged in a six-dimensional…
The availability of large datasets containing stellar parameters, distances, and extinctions for stars in the Milky Way, particularly within the Galactic disk, is essential for advancing our understanding of the Galaxy's stellar…
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…
As a well-established procedure for the vast majority of normal main-sequence stars, determination of atmospheric and stellar parameters turns to be a challenging process in case of magnetic chemically peculiar stars. Inhomogeneous…
Large exoplanet surveys have successfully detected thousands of exoplanets to-date. Utilizing these detections and non-detections to constrain our understanding of the formation and evolution of planetary systems also requires a detailed…
Accurate determination of stellar atmospheric parameters and elemental abundances is crucial for Galactic archeology via large-scale spectroscopic surveys. In this paper, we estimate stellar atmospheric parameters -- effective temperature…
Planetary studies demand precise and accurate stellar parameters as input to infer the planetary properties. Different methods often provide different results that could lead to biases in the planetary parameters. In this work, we present a…
Context: Traditional one-dimensional (1D) hydrostatic model atmospheres introduce systematic modelling errors into spectroscopic analyses of FGK-type stars. Aims: We present an updated version of the STAGGER-grid of 3D model atmospheres,…
The stellar atmospheric parameters and physical properties of stars in the Kepler Input Catalog (KIC) are of great significance for the study of exoplanets, stellar activity, and asteroseismology. However, despite extensive effort over the…
Extremely metal-poor (EMP) stars ([Fe/H] < -3.0 dex) provide a unique window into understanding the first generation of stars and early chemical enrichment of the Universe. EMP stars are exceptionally rare, however, and the relatively small…
We aim to prepare the machine-learning ground for the next generation of spectroscopic surveys, such as 4MOST and WEAVE. Our goal is to show that convolutional neural networks can predict accurate stellar labels from relevant spectral…
We calculate the fundamental stellar parameters effective temperature, surface gravity and iron abundance - T$_{\rm eff}$, log g, [Fe/H] - for the final release of the Mapping Nearby Galaxies at APO (MaNGA) Stellar Library (MaStar),…
We carried out a critical appraisal of the two theoretical models, Kurucz' ATLAS9 and PHOENIX/NextGen, for stellar atmosphere synthesis. Our tests relied on the theoretical fit of SEDs for a sample of 334 target stars along the whole…