# Classifying galaxies according to their HI content

## TL;DR

This paper develops and evaluates machine learning classifiers to determine whether galaxies are HI rich or poor, using simulation data and real observations, with high accuracy especially at higher redshifts.

## Contribution

It introduces a robust machine learning pipeline for classifying galaxy HI content, validated on both simulated and real survey data, enhancing future large-scale HI surveys.

## Key findings

- Random Forest classifier achieves over 98.6% accuracy at z=0
- SVM classifier attains over 87.6% precision on real data
- Classifier performance improves with increasing redshift

## Abstract

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.