A Photometric Machine-Learning Method to Infer Stellar Metallicity
- Creators
- Miller, Adam A.
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
- Eprint ID
- 61310
- DOI
- 10.1007/978-3-319-16313-0_17
- Resolver ID
- CaltechAUTHORS:20151020-090202714
- HST-HF-51325.01
- NASA Hubble Fellowship
- NAS 5-26555
- NASA
- NASA/JPL/Caltech
- Created
-
2015-10-20Created from EPrint's datestamp field
- Updated
-
2021-11-10Created from EPrint's last_modified field
- Series Name
- Lecture Notes in Computer Science
- Series Volume or Issue Number
- 8999