A re-assessment 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 (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.

15 pages, 8 figures, 13 tables (MNRAS accepted)

References in corpus (13)

• The Host Galaxies and Classification of Active Galactic Nuclei
• Understanding Galaxy Evolution through Emission Lines
• New fully empirical calibrations of strong-line metallicity indicators in star forming galaxies
• Deep learning predictions of galaxy merger stage and the importance of observational realism
• Galaxy Merger Rates up to z $\sim$ 3 using a Bayesian Deep Learning Model $-$ A Major-Merger classifier using IllustrisTNG Simulation data
• The ALMaQUEST Survey: III. Scatter in the resolved star forming main sequence is primarily due to variations in star formation efficiency
• [ArIII]/[OIII] and [SIII]/[OIII]: well-behaved oxygen abundance indicators for HII regions and star forming galaxies
• What shapes a galaxy? - Unraveling the role of mass, environment and star formation in forming galactic structure
• The EDGE-CALIFA Survey: Exploring the Star Formation Law through Variable Selection
• A Machine Learning Artificial Neural Network Calibration of the Strong-Line Oxygen Abundance
• Using photo-ionisation models to derive carbon and oxygen gas-phase abundances in the rest UV
• Simultaneous Estimation of Photometric Redshifts and SED Parameters: Improved Techniques and a Realistic Error Budget
• Pattern recognition in the ALFALFA.70 and Sloan Digital Sky Surveys: A catalog of $\sim$ 500,000 HI gas fraction estimates based on artificial neural networks

Cited by in corpus (12)

• A Comprehensive Measurement of the Local Value of the Hubble Constant with 1 km/s/Mpc Uncertainty from the Hubble Space Telescope and the SH0ES Team
• On the origin of the mass-metallicity gradient relation in the local Universe
• Measurements of the Hubble Constant with a Two Rung Distance Ladder: Two Out of Three Ain't Bad
• What drives the scatter of local star-forming galaxies in the BPT diagrams? A Machine Learning based analysis
• Mapping the Diversity of Galaxy Spectra with Deep Unsupervised Machine Learning
• Absolute Calibration of Cepheid Period-Luminosity Relations in NGC 4258
• How well do local relations predict gas-phase metallicity gradients? Results from SDSS-IV MaNGA
• The role of gas kinematics in setting metallicity gradients at high redshift
• Conversions between gas-phase metallicities in MaNGA
• Investigating the Origin of Observed Central Dips in Radial Metallicity Profiles
• Formation and fate of low metallicity stars in IllustrisTNG50
• First joint absorption and T${}_e$-based metallicity measured in a GRB host galaxy at $z=4.28$ using JWST/NIRSpec