A machine learning approach to photometric metallicities of giant stars

Journal Article · Fri Sep 09 00:00:00 EDT 2022 · Monthly Notices of the Royal Astronomical Society

DOI:https://doi.org/10.1093/mnras/stac2550· OSTI ID:1890018

Fallows, Connor P.; Sanders, Jason L.

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 photoastrometric 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 utilizing the first-stage parallax estimates. The algorithm produces metallicity predictions with an average uncertainty of $$\pm 0.19\, \mathrm{dex}$$. 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 ± 0.002 dex kpc^–1) and the vertex deviation of the bar ($$-21.29\pm 2.74\, \mathrm{deg}$$).

• Research Organization: US Department of Energy (USDOE), Washington, DC (United States). Office of Science, Sloan Digital Sky Survey (SDSS)
• Sponsoring Organization: USDOE
• OSTI ID: 1890018
• Alternate ID(s): OSTI ID: 1982666
• Journal Information: Monthly Notices of the Royal Astronomical Society, Journal Name: Monthly Notices of the Royal Astronomical Society Vol. 516 Journal Issue: 4; ISSN 0035-8711
• Publisher: Oxford University Press
• Country of Publication: United Kingdom
• Language: English

Related Subjects

79 ASTRONOMY AND ASTROPHYSICS
Astronomy & Astrophysics
methods: statistical
stars: distances
Galaxy: abundances
Galaxy: bulge
Galaxy: stellar content