Transferring spectroscopic stellar labels to 217 million Gaia DR3 XP stars with SHBoost

¹ Leibniz-Institut für Astrophysik Potsdam (AIP), An der Sternwarte 16, 14482 Potsdam, Germany
² Departament de Física Quàntica i Astrofísica (FQA), Universitat de Barcelona, C Martí i Franquès, 1, 08028 Barcelona, Spain
³ Institut de Ciències del Cosmos (ICCUB), Universitat de Barcelona (UB), C Martí i Franquès, 1, 08028 Barcelona, Spain
⁴ Institut d’Estudis Espacials de Catalunya (IEEC), Edifici RDIT, Campus UPC, 08860 Castelldefels (Barcelona), Spain
⁵ Instituto de Astrofísica de Canarias, 38200 La Laguna, Tenerife, Spain
⁶ Departamento de Astrofísica, Universidad de La Laguna, 38205 La Laguna, Tenerife, Spain
⁷ Institut für Physik und Astronomie, Universität Potsdam, Haus 28 Karl-Liebknecht-Str. 24/25, 14476 Golm, Germany
⁸ INAF – Osservatorio Astronomico di Padova, Vicolo dell’Osservatorio 5, 35122 Padova, Italy
⁹ Zentrum für Astronomie der Universität Heidelberg, Landessternwarte, Königstuhl 12, 69117 Heidelberg, Germany
¹⁰ Max Planck Institute for Astronomy, Königstuhl 17, 69117 Heidelberg, Germany
¹¹ CIGUS CITIC – Department of Computer Science and Information Technologies, University of A Coruña, Campus de Elviña s/n, A Coruña, 15071, Spain
¹² Escuela Superior de Ingeniería y Tecnología, Universidad Internacional de la Rioja, Spain
¹³ Escuela de Arquitectura y Politécnica, Universidad Europea de Valencia, Spain
¹⁴ Departamento de Astrofísica, Centro de Astrobiología (CSIC-INTA), Camino Bajo del Castillo s/n, 28692 Villanueva de la Cañada, Madrid, Spain
¹⁵ Department of Astrophysics, University of Vienna, Türkenschanzstraße 17, 1180 Wien, Austria
¹⁶ Lund Observatory, Division of Astrophysics, Department of Physics, Lund University, Box 43, 22100 Lund, Sweden
¹⁷ GEPI, Observatoire de Paris, Université PSL, CNRS, 5 Place Jules Janssen, 92190 Meudon, France

^★ Corresponding author; fanders@icc.ub.edu

Received: 8 July 2024
Accepted: 20 September 2024

Abstract

With Gaia Data Release 3 (DR3), new and improved astrometric, photometric, and spectroscopic measurements for 1.8 billion stars have become available. Alongside this wealth of new data, however, there are challenges in finding efficient and accurate computational methods for their analysis. 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. To this end, we built a stable gradient-boosted random-forest regressor (xgboost), trained on spectroscopic data, capable of producing output parameters with reliable uncertainties from Gaia DR3 data (most notably the low-resolution XP spectra), without ground-based spectroscopic observations. Using Shapley additive explanations, we interpret how the predictions for each star are influenced by each data feature. For the training and testing of the network, we used high-quality parameters obtained from the StarHorse code for a sample of around eight million stars observed by major spectroscopic stellar surveys, complemented by curated samples of hot stars, very metal-poor stars, white dwarfs, and hot sub-dwarfs. The training data cover the whole sky, all Galactic components, and almost the full magnitude range of the Gaia DR3 XP sample of more than 217 million objects that also have reported parallaxes. We have achieved median uncertainties of 0.20 mag in V-band extinction, 0.01 dex in logarithmic effective temperature, 0.20 dex in surface gravity, 0.18 dex in metallicity, and 12% in mass (over the full Gaia DR3 XP sample, with considerable variations in precision as a function of magnitude and stellar type). We succeeded in predicting competitive results based on Gaia DR3 XP spectra compared to classical isochrone or spectral-energy distribution fitting methods we employed in earlier works, especially for parameters A_V and T_eff, along with the metallicity values. Finally, we showcase some potential applications of this new catalogue, including extinction maps, metallicity trends in the Milky Way, and extended maps of young massive stars, metal-poor stars, and metal-rich stars.