An application of machine learning techniques to galaxy cluster mass
  estimation using the MACSIS simulations
  Machine learning (ML) techniques, in particular supervised regression
algorithms, are a promising new way to use multiple observables to predict a
cluster's mass or other key features. To investigate this approach we use the
\textsc{MACSIS} sample of simulated hydrodynamical galaxy clusters to train a
variety of ML models, mimicking different datasets. We find that compared to
predicting the cluster mass from the $\sigma -M$ relation, the scatter in the
predicted-to-true mass ratio is reduced by a factor of 4, from $0.130\pm0.004$
dex (${\simeq} 35$ per cent) to $0.031 \pm 0.001$ dex (${\simeq} 7$ per cent)
when using the same, interloper contaminated, spectroscopic galaxy sample.
Interestingly, omitting line-of-sight galaxy velocities from the training set
has no effect on the scatter when the galaxies are taken from within
$r_{200c}$. We also train ML models to reproduce estimated masses derived from
mock X-ray and weak lensing analyses. While the weak lensing masses can be
recovered with a similar scatter to that when training on the true mass, the
hydrostatic mass suffers from significantly higher scatter of ${\simeq} 0.13$
dex (${\simeq} 35$ per cent). Training models using dark matter only
simulations does not significantly increase the scatter in predicted cluster
mass compared to training on simulated clusters with hydrodynamics. In summary,
we find ML techniques to offer a powerful method to predict masses for large
samples of clusters, a vital requirement for cosmological analysis with future
surveys.
