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Published 2015 | public
Book Section - Chapter

A Photometric Machine-Learning Method to Infer Stellar Metallicity

Abstract

Following its formation, a star's metal content is one of the few factors that can significantly alter its evolution. Measurements of stellar metallicity ([Fe/H]) typically require a spectrum, but spectroscopic surveys are limited to a few×10^6 targets; photometric surveys, on the other hand, have detected > 10^9 stars. I present a new machine-learning method to predict [Fe/H] from photometric colors measured by the Sloan Digital Sky Survey (SDSS). The training set consists of ~120,000 stars with SDSS photometry and reliable [Fe/H] measurements from the SEGUE Stellar Parameters Pipeline (SSPP). For bright stars (g′ ≤ 18 mag), with 4500 K ≤ t_(eff) ≤ 7000 K, corresponding to those with the most reliable SSPP estimates, I find that the model predicts [Fe/H] values with a root-mean-squared-error (RMSE) of ~0.27 dex. The RMSE from this machine-learning method is similar to the scatter in [Fe/H] measurements from low-resolution spectra.

Additional Information

© 2015 Springer International Publishing Switzerland. I am thankful to Brian Bue and Umaa Rebbapragada for fruitful conversations on model selection. I am grateful for support from a NASA Hubble Fellowship grant: HST-HF-51325.01, awarded by STScI, operated by AURA, Inc., for NASA, under contract NAS 5-26555. This research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration.

Additional details

Created:
August 20, 2023
Modified:
January 13, 2024