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A Photometric Machine-Learning Method to Infer Stellar MetallicityFollowing 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 x 10(exp 6) targets; photometric surveys, on the other hand, have detected > 10(exp 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 approx. 120,000 stars with SDSS photometry and reliable [Fe/H] measurements from the SEGUE Stellar Parameters Pipeline (SSPP). For bright stars (g' < or = 18 mag), with 4500 K < or = Teff < or = 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 approx.0.27 dex. The RMSE from this machine-learning method is similar to the scatter in [Fe/H] measurements from low-resolution spectra..
Document ID
Document Type
Conference Paper
External Source(s)
Miller, Adam A. (California Inst. of Tech. Pasadena, CA, United States)
Date Acquired
August 1, 2017
Publication Date
March 23, 2015
Subject Category
Meeting Information
Data Analytics in Astronomy and Sciences Symposium(Aizu Univ.)
Distribution Limits
machine learning
photometric surveys
stellar metallicity
random forest