A re-assessment of strong line metallicity conversions in the machine learning era

TL;DR

This paper updates and improves strong line metallicity calibration conversions using newer data, additional diagnostics, and introduces a machine learning approach with random forests that offers greater flexibility and accuracy.

Contribution

It provides an updated set of polynomial coefficients and introduces a novel random forest method for metallicity calibration conversions, enhancing accuracy and usability.

Findings

Random forest achieves comparable accuracy to polynomial methods.

The random forest model is applicable across a wide metallicity range.

Updated calibrations are based on larger, more recent galaxy datasets.

Abstract

Strong line metallicity calibrations are widely used to determine the gas phase metallicities of individual HII regions and entire galaxies. Over a decade ago, based on the Sloan Digital Sky Survey Data Release 4 (SDSS DR4), Kewley \& Ellison published the coefficients of third-order polynomials that can be used to convert between different strong line metallicity calibrations for global galaxy spectra. Here, we update the work of Kewley \& Ellison in three ways. First, by using a newer data release (DR7), we approximately double the number of galaxies used in polynomial fits, providing statistically improved polynomial coefficients. Second, we include in the calibration suite five additional metallicity diagnostics that have been proposed in the last decade and were not included by Kewley \& Ellison. Finally, we develop a new machine learning approach for converting between metallicity calibrations. The random forest algorithm is non-parametric and therefore more flexible than polynomial conversions, due to its ability to capture non-linear behaviour in the data. The random forest method yields the same accuracy as the (updated) polynomial conversions, but has the significant advantage that a single model can be applied over a wide range of metallicities, without the need to distinguish upper and lower branches in $R_{23}$ calibrations. The trained random forest is made publicly available for use in the community.