The <i>Gaia</i>-ESO Public Spectroscopic Survey: Motivation, implementation, GIRAFFE data processing, analysis, and final data products

Journal article

We present a machine learning method to assign stellar parameters (temperature, surface gravity, metallicity) to the photometric data of large photometric surveys such as SDSS and SKYMAPPER. The method makes use of our previous effort in homogenizing and recalibrating spectroscopic data from surveys like APOGEE, GALAH, or LAMOST into a single catalog, which is used to inform a neural network. We obtain spectroscopic-quality parameters for millions of stars that have only been observed photometrically. The typical uncertainties are of the order of 100K in temperature, 0.1 dex in surface gravity, and 0.1 dex in metallicity and the method performs well down to low metallicity, were obtaining reliable results is known to be difficult

Authors: Gilmore, G; Randich, S; Worley, CC; Hourihane, A; Gonneau, A

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: EDP Sciences
• Journal: Astronomy & Astrophysics
• Volume: 666
• Pages: A120-A120
• Publication date: 2022-08-06
• Acceptance date: 2022-07-10
• DOI: 10.1051/0004-6361/202243134
• EISSN: 1432-0746
• ISSN: 0004-6361
• Language: English
• Keywords: Stars; Computer science; Artificial intelligence; Geography; Galaxy; Context (archaeology); Cartography; Astronomy; Benchmark (surveying); Physics; Data set; Astrophysics