A reassessment of strong line metallicity conversions in the machine learning era

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, 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, 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 (RF) algorithm is non-parametric and therefore more flexible than polynomial conversions, due to its ability to capture non-linear behaviour in the data. The RF 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 RF is made publicly available for use in the community.

Publisher

Oxford University Press

Publication Date

2-18-2021

Publication Title

Monthly Notices of the Royal Astronomical Society

Department

Physics and Astronomy

Additional Department

Gender, Sexuality, and Feminist Studies

Document Type

Article

DOI

https://dx.doi.org/10.1093/mnras/stab466

Keywords

Galaxies: Fundamental parameters, Galaxies: Abundances, Methods: Data analysis, Methods: Statistical, Methods: Oberservational, Methods: Numerical

Language

English

Format

text

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