Related papers: Machine Learning Regression of extinction in the s…
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:…
In this paper, we explore the feasibility of using machine learning regression as a method of extracting basic stellar parameters and line-of-sight extinctions from spectro-photometric data. We built a stable gradient-boosted random-forest…
Machine learning has increasingly gained more popularity with its incredibly powerful ability to make predictions or calculated suggestions for large amounts of data. We apply the machine learning classification to 85,613,922 objects in the…
The Gaia Data Release 3 (DR3), published in June 2022, delivers a diverse set of astrometric, photometric, and spectroscopic measurements for more than a billion stars. The wealth and complexity of the data makes traditional approaches for…
The second Gaia data release (Gaia-DR2) contains, beyond the astrometry, three-band photometry for 1.38 billion sources. We have used these three broad bands to infer stellar effective temperatures, Teff, for all sources brighter than G=17…
Gaia will obtain astrometry and spectrophotometry for essentially all sources in the sky down to a broad band magnitude limit of G=20, an expected yield of 10^9 stars. Its main scientific objective is to reveal the formation and evolution…
The scarcity of high-fidelity extinction measurements remains a bottleneck in deriving accurate stellar properties from Gaia parallaxes. In this work, we aim to derive precision extinction estimates for APOGEE DR19 stars, establishing a new…
Large spectroscopic surveys have enabled in the recent years the computation of three-dimensional interstellar extinction maps thanks to accurate stellar atmospheric parameters and line-of-sight distances. Such maps are complementary to 3D…
The first Gaia data release unlocked the access to the photometric information of 1.1 billion sources in the $G$-band. Yet, given the high level of degeneracy between extinction and spectral energy distribution for large passbands such as…
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.…
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.…
The observation of our home galaxy, the Milky Way (MW), is made difficult by our internal viewpoint. The Gaia survey that contains around 1.6 billion star distances is the new flagship of MW structure and can be combined with other…
Combining the precise parallaxes and optical photometry delivered by Gaia's second data release (Gaia DR2) with the photometric catalogues of PanSTARRS-1, 2MASS, and AllWISE, we derive Bayesian stellar parameters, distances, and extinctions…
For the vast majority of stars in the second Gaia data release, reliable distances cannot be obtained by inverting the parallax. A correct inference procedure must instead be used to account for the nonlinearity of the transformation and…
Although Gaia has identified the central stars of planetary nebulae (CSPNe) for about 70% of known Galactic planetary nebulae (PNe), reliable distance estimates remain incomplete, with fewer than one quarter having accurate parallaxes.…
The Gaia Galactic survey mission is designed and optimized to obtain astrometry, photometry, and spectroscopy of nearly two billion stars in our Galaxy. Yet as an all-sky multi-epoch survey, Gaia also observes several million extragalactic…
A precise interstellar dust extinction law is critically important to interpret observations. There are two indicators of extinction: the color excess ratio (CER) and the relative extinction. Compared to the CER, the wavelength-dependent…
Gaia Data Release 3 contains a wealth of new data products for the community. Astrophysical parameters are a major component of this release. They were produced by the Astrophysical parameters inference system (Apsis) within the Gaia Data…
We provide classifications for all 143 million non-repeat photometric objects in the Third Data Release of the Sloan Digital Sky Survey (SDSS) using decision trees trained on 477,068 objects with SDSS spectroscopic data. We demonstrate that…
We present the General Stellar Parameterizer from Photometry (GSP-Phot), which is part of the astrophysical parameters inference system (Apsis). GSP-Phot is designed to produce a homogeneous catalogue of parameters for hundreds of millions…