A machine learning approach to photometric metallicities of giant stars

Abstract

Despite the advances provided by large-scale photometric surveys, stellar features - such as metallicity - generally remain limited to spectroscopic observations often of bright, nearby low-extinction stars. To rectify this, we present a neural network approach for estimating the metallicities and distances of red giant stars with 8-band photometry and parallaxes from Gaia EDR3 and the 2MASS and WISE surveys. The algorithm accounts for uncertainties in the predictions arising from the range of possible outputs at each input and from the range of models compatible with the training set (through drop-out). A two-stage procedure is adopted where an initial network to estimate photo-astrometric parallaxes is trained using a large sample of noisy parallax data from Gaia EDR3 and then a secondary network is trained using spectroscopic metallicities from the APOGEE and LAMOST surveys and an augmented feature space utilising the first-stage parallax estimates. The algorithm produces metallicity predictions with an average uncertainty of $\pm 0.19$ dex. The methodology is applied to stars within the Galactic bar/bulge with particular focus on a sample of 1.69 million objects with Gaia radial velocities. We demonstrate the use and validity of our approach by inspecting both spatial and kinematic gradients with metallicity in the Galactic bar/bulge recovering previous results on the vertical metallicity gradient ($-0.528\pm 0.002$ dex/kpc) and the vertex deviation of the bar ($-21.29\pm 2.74$ deg).

18 pages, 14 figures, 3 tables, accepted for publication in MNRAS

References in corpus (20)

• The Gaia mission
• A grid of MARCS model atmospheres for late-type stars I. Methods and general properties
• Estimating distances from parallaxes. V: Geometric and photogeometric distances to 1.47 billion stars in Gaia Early Data Release 3
• Gaia Early Data Release 3: Parallax bias versus magnitude, colour, and position
• Gaia Early Data Release 3: Photometric content and validation
• The GALAH+ Survey: Third Data Release
• The Milky Way Tomography with SDSS: II. Stellar Metallicity
• Evidence for Two Early Accretion Events That Built the Milky Way Stellar Halo
• The Optical to Mid-Infrared Extinction Law Based on the APOGEE, Gaia DR2, Pan-STARRS1, SDSS, APASS, 2MASS and WISE Surveys
• The unWISE Catalog: Two Billion Infrared Sources from Five Years of WISE Imaging
• The Dark Energy Survey Data Release 2
• 4MOST: Project overview and information for the First Call for Proposals
• Photo-astrometric distances, extinctions, and astrophysical parameters for Gaia EDR3 stars brighter than G=18.5
• The Best and Brightest Metal-Poor Stars
• The Pristine Inner Galaxy Survey (PIGS) I: Tracing the kinematics of metal-poor stars in the Galactic bulge
• Beyond spectroscopy. I. Metallicities, distances, and age estimates for over twenty million stars from SMSS DR2 and Gaia EDR3
• Gaia Early Data Release 3: Updated radial velocities from Gaia DR2
• Magellanic Mayhem: Metallicities and Motions
• Exposing Sgr tidal debris behind the Galactic disk with M giants selected in WISE$\cap$2MASS
• The Metal-Poor Metallicity Distribution of the Ancient Milky Way

Cited by in corpus (3)

• Transferring spectroscopic stellar labels to 217 million Gaia DR3 XP stars with SHBoost
• Photometric metallicity for 694233 Galactic giant stars from Gaia DR3 synthetic Stromgren photometry. Metallicity distribution functions of halo sub-structures
• Stellar atmospheric parameters and chemical abundances of about 5 million stars from S-PLUS multi-band photometry