A Machine Learning Artificial Neural Network Calibration of the
  Strong-Line Oxygen Abundance
  The HII 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 HII region spectra with auroral line detections, I build
multi-layer 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 HII region and also the challenges faced.
