Galaxy cluster mass estimation with deep learning and hydrodynamical
  simulations
  We evaluate the ability of Convolutional Neural Networks (CNNs) to predict
galaxy cluster masses in the BAHAMAS hydrodynamical simulations. We train four
separate single-channel networks using: stellar mass, soft X-ray flux,
bolometric X-ray flux, and the Compton $y$ parameter as observational tracers,
respectively. Our training set consists of $\sim$4800 synthetic cluster images
generated from the simulation, while an additional $\sim$3200 images form a
validation set and a test set, each with 1600 images. In order to mimic real
observation, these images also contain uncorrelated structures located within
50 Mpc in front and behind clusters and seen in projection, as well as
instrumental systematics including noise and smoothing. In addition to CNNs for
all the four observables, we also train a `multi-channel' CNN by combining the
four observational tracers. The learning curves of all the five CNNs converge
within 1000 epochs. The resulting predictions are especially precise for halo
masses in the range $10^{13.25}M_{\odot}<M<10^{14.5}M_{\odot}$, where all five
networks produce mean mass biases of order $\approx$1\% with a scatter of
$\lesssim$20\%. The network trained with Compton $y$ parameter maps yields the
most precise predictions. We interpret the network's behaviour using two
diagnostic tests to determine which features are used to predict cluster mass.
The CNN trained with stellar mass images detect galaxies (not surprisingly),
while CNNs trained with gas-based tracers utilise the shape of the signal to
estimate cluster mass.
