Predicting the Neutral Hydrogen Content of Galaxies From Optical Data
  Using Machine Learning
  We develop a machine learning-based framework to predict the HI content of
galaxies using more straightforwardly observable quantities such as optical
photometry and environmental parameters. We train the algorithm on z=0-2
outputs from the Mufasa cosmological hydrodynamic simulation, which includes
star formation, feedback, and a heuristic model to quench massive galaxies that
yields a reasonable match to a range of survey data including HI. We employ a
variety of machine learning methods (regressors), and quantify their
performance using the root mean square error ({\sc rmse}) and the Pearson
correlation coefficient (r). Considering SDSS photometry, 3$^{rd}$ nearest
neighbor environment and line of sight peculiar velocities as features, we
obtain r $> 0.8$ accuracy of the HI-richness prediction, corresponding to {\sc
rmse}$<0.3$. Adding near-IR photometry to the features yields some improvement
to the prediction. Compared to all the regressors, random forest shows the best
performance, with r $>0.9$ at $z=0$, followed by a Deep Neural Network with r
$>0.85$. All regressors exhibit a declining performance with increasing
redshift, which limits the utility of this approach to $z\la 1$, and they tend
to somewhat over-predict the HI content of low-HI galaxies which might be due
to Eddington bias in the training sample. We test our approach on the RESOLVE
survey data. Training on a subset of RESOLVE data, we find that our machine
learning method can reasonably well predict the HI-richness of the remaining
RESOLVE data, with {\sc rmse}$\sim0.28$. When we train on mock data from Mufasa
and test on RESOLVE, this increases to {\sc rmse}$\sim0.45$. Our method will be
useful for making galaxy-by-galaxy survey predictions and incompleteness
corrections for upcoming HI 21cm surveys such as the LADUMA and MIGHTEE surveys
on MeerKAT, over regions where photometry is already available.
