A machine learning artificial neural network calibration of the strong-line oxygen abundance
Abstract
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.
- Publication:
-
Monthly Notices of the Royal Astronomical Society
- Pub Date:
- May 2019
- DOI:
- 10.1093/mnras/stz649
- arXiv:
- arXiv:1903.01506
- Bibcode:
- 2019MNRAS.485.3569H
- Keywords:
-
- methods: data analysis;
- ISM: abundances;
- H II regions;
- galaxies: ISM;
- Astrophysics - Astrophysics of Galaxies
- E-Print:
- 12 pages, 15 figures. Accepted to MNRAS