Using Colors to Improve Photometric Metallicity Estimates for Galaxies

TL;DR

This paper demonstrates that galaxy color, combined with luminosity, significantly improves photometric metallicity estimates, reducing errors and biases compared to using luminosity alone, based on analysis of SDSS data.

Contribution

It introduces empirical LZC relations that incorporate color into photometric metallicity estimates, enhancing accuracy and bias correction over previous methods.

Findings

Photometric metallicity estimates are 50% more precise with color inclusion.

Galaxy metallicity can be estimated within 0.05-0.1 dex of spectroscopic values.

Including color reduces systematic biases in metallicity estimates.

Abstract

There is a well known correlation between the mass and metallicity of star-forming galaxies. Because mass is correlated with luminosity, this relation is often exploited, when spectroscopy is not available, to estimate galaxy metallicities based on single band photometry. However, we show that galaxy color is typically more effective than luminosity as a predictor of metallicity. This is a consequence of the correlation between color and the galaxy mass-to-light ratio and the recently discovered correlation between star formation rate (SFR) and residuals from the mass-metallicity relation. Using Sloan Digital Sky Survey spectroscopy of 180,000 nearby galaxies, we derive "LZC relations," empirical relations between metallicity (in seven common strong line diagnostics), luminosity, and color (in ten filter pairs and four methods of photometry). We show that these relations allow photometric metallicity estimates, based on luminosity and a single optical color, that are 50% more precise than those made based on luminosity alone; galaxy metallicity can be estimated to within 0.05 - 0.1 dex of the spectroscopically-derived value depending on the metallicity diagnostic used. Including color information in metallicity estimates also reduces systematic biases for populations skewed toward high or low SFR environments, as we illustrate using the host galaxy of the supernova SN 2010ay. This new tool will lend more statistical power to studies of galaxy populations, such as supernova and gamma-ray burst (GRB) host environments, in ongoing and future wide field imaging surveys.