Classifying galaxies according to their HI content
  We use machine learning to classify galaxies according to their HI 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 HI content using the same input features, HI 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 HI rich ($\rm{log(M_{HI}/M_{*})} > -2 $) before
estimating its neutral hydrogen content using the regressors developed in the
first paper. We resort to various machine learning algorithms and assess their
performance with various metrics such as accuracy for instance. The performance
of the classifiers gets better with increasing redshift and reaches their peak
performance around $z = 1$. Random Forest method, the most robust among the
classifiers when considering only the mock data for both training and test in
this study, reaches an accuracy above $98.6 \%$ at $z = 0$ and above $99.0 \%$
at $z = 1$. We test our algorithms, trained with simulation data, on
classification of the galaxies in RESOLVE, ALFALFA and GASS surveys.
Interestingly, SVM algorithm, the best classifier for the tests, achieves a
precision, the relevant metric for the tests, above $87.60\%$ and a specificity
above $71.4\%$ with all the tests, indicating that the classifier is capable of
learning from the simulated data to classify HI rich/HI poor galaxies from the
real observation data. With the advent of large HI 21 cm surveys such as the
SKA, this set of classifiers, together with the regressors developed in the
first paper, will be part of a pipeline, a very useful tool, which is aimed at
predicting HI content of galaxies.
