Related papers: Machine Learning Regression of stellar effective t…

Context: New spectroscopic surveys will increase the number of astronomical objects requiring characterization by over tenfold.. Machine learning tools are required to address this data deluge in a fast and accurate fashion. Most machine…

The second Gaia data release, DR2, contained radial velocities of stars with effective temperatures up to Teff = 6900 K. The third data release, Gaia DR3, extends this up to Teff = 14,500 K. We derive the radial velocities for hot stars…

Astrometric surveys provide the opportunity to measure the absolute magnitudes of large numbers of stars, but only if the individual line-of-sight extinctions are known. Unfortunately, extinction is highly degenerate with stellar effective…

GALAH is a large-scale magnitude-limited southern stellar spectroscopic survey. Its second data release (GALAH DR2) provides values of stellar parameters and abundances of 23 elements for 342,682 stars (Buder et al.). Here we add a…

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…

This paper is the second in a series, implementing a classification system for Gaia observations of unresolved galaxies. Our goals are to determine spectral classes and estimate intrinsic astrophysical parameters via synthetic templates.…

Aims. We aim at deriving stellar atmospheric parameters based on the photometric data from the Javalambre Photometric Local Universe Survey (J-PLUS) in addition to near-infrared photometry from the Two Micron All-Sky Survey (2MASS).…

We aimed to assess the accuracy of the Gaia teff and logg estimates as derived with current models and observations. We assessed the validity of several inference techniques for deriving the physical parameters of ultra-cool dwarf stars. We…

The inference of stellar parameters (such as radius and mass) through asteroseismic forward modelling depends on the number, accuracy, and precision of seismic and atmospheric constraints. ESA's Gaia space mission is providing precise…

In this paper we describe Kea a new spectroscopic fitting method to derive stellar parameters from moderate to low signal/noise, high-resolution spectra. We developed this new tool to analyze the massive data set of the Kepler mission…

Gaia Data Release 1 allows to recalibrate standard candles such as the Red Clump stars. To use those, they first need to be accurately characterised. In particular, colours are needed to derive the interstellar extinction. As no filter is…

The second Gaia data release (DR2), contains very precise astrometric and photometric properties for more than one billion sources, astrophysical parameters for dozens of millions, radial velocities for millions, variability information for…

Context. In the current ever increasing data volumes of astronomical surveys, automated methods are essential. Objects of known classes from the literature are necessary for training supervised machine learning algorithms, as well as for…

Studies of Galactic structure and evolution have benefitted enormously from Gaia kinematic information, though additional, intrinsic stellar parameters like age are required to best constrain Galactic models. Asteroseismology is the most…

Mass loss is a key aspect of stellar evolution, particularly in evolved massive stars, yet episodic mass loss remains poorly understood. To investigate this, we need evolved massive stellar populations across various galactic environments.…

We develop a new machine learning algorithm, Via Machinae, to identify cold stellar streams in data from the Gaia telescope. Via Machinae is based on ANODE, a general method that uses conditional density estimation and sideband…

We develop, validate and apply a forward model to estimate stellar atmospheric parameters ($T_{\rm eff}$, $\log{g}$ and $\mathrm{[Fe/H]}$), revised distances and extinctions for 220 million stars with XP spectra from $\textit{Gaia}$ DR3.…

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 Gaia-ESO Survey (GES) is a large public spectroscopic survey that has collected, over a period of 6 years, spectra of ~ 10^5 stars. This survey provides not only the reduced spectra, but also the stellar parameters and abundances…

Gaia Bp/Rp spectra for over two hundred million stars have great potential for mapping metallicity across the Milky Way. We aim to construct an alternative catalog of atmospheric parameters from Gaia Bp/Rp spectra by fitting them with…