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2019MNRAS.485.3569H - Mon. Not. R. Astron. Soc., 485, 3569-3579 (2019/May-3)

A machine learning artificial neural network calibration of the strong-line oxygen abundance.

HO I.-T.

Abstract (from CDS):

The H II region oxygen abundance is a key observable for studying chemical properties of galaxies. Deriving oxygen abundances using optical spectra often relies on empirical strong-line calibrations calibrated to the direct method. Existing calibrations usually adopt linear or polynomial functions to describe the non-linear relationships between strong-line ratios and Te oxygen abundances. Here, I explore the possibility of using an artificial neural network model to construct a non-linear strong-line calibration. Using about 950 literature H II region spectra with auroral line detections, I build multilayer perceptron models under the machine learning framework of training and testing. I show that complex models, like the neural network, are preferred at the current sample size and can better predict oxygen abundance than simple linear models. I demonstrate that the new calibration can reproduce metallicity gradients in nearby galaxies and the mass-metallicity relationship. Finally, I discuss the prospects of developing new neural network calibrations using forthcoming large samples of H II region and also the challenges faced.

Abstract Copyright: © 2019 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical Society

Journal keyword(s): methods: data analysis - ISM: abundances - H ii regions - galaxies: ISM

Simbad objects: 6

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