Classifying galaxies according to their H I content

Abstract

We use machine learning to classify galaxies according to their H I content, based on both their optical photometry and environmental properties. The data used for our analyses are the outputs in the range z = 0–1 from MUFASA cosmological hydrodynamic simulation. In our previous paper, where we predicted the galaxy H I content using the same input features, H I-rich galaxies were only selected for the training. In order for the predictions on real observation data to be more accurate, the classifiers built in this study will first establish if a galaxy is H I rich (⁠log(MHI/M∗)>−2⁠) before estimating its neutral hydrogen content using the regressors developed in the first paper.