Prediction of galaxy halo masses in SDSS DR7 via a machine learning
  approach
  We present a machine learning (ML) approach for the prediction of galaxies'
dark matter halo masses that achieves an improved performance over conventional
methods. We train three ML algorithms (\texttt{XGBoost}, Random Forests, and
neural network) to predict halo masses using a set of synthetic galaxy
catalogues that are built by populating dark matter haloes in N-body
simulations with galaxies, and that match both the clustering and the
joint-distributions of properties of galaxies in the Sloan Digital Sky Survey
(SDSS). We explore the correlation of different galaxy- and group-related
properties with halo mass, and extract the set of nine features that contribute
the most to the prediction of halo mass. We find that mass predictions from the
ML algorithms are more accurate than those from halo abundance matching
(\texttt{HAM}) or dynamical mass (\texttt{DYN}) estimates. Since the danger of
this approach is that our training data might not accurately represent the real
Universe, we explore the effect of testing the model on synthetic catalogues
built with different assumptions than the ones used in the training phase. We
test a variety of models with different ways of populating dark matter haloes,
such as adding velocity bias for satellite galaxies. We determine that, though
training and testing on different data can lead to systematic errors in
predicted masses, the ML approach still yields substantially better masses than
either \texttt{HAM}or \texttt{DYN}. Finally, we apply the trained model to a
galaxy and group catalogue from the SDSS DR7 and present the resulting halo
masses.
