Machine Learning to identify ICL and BCG in simulated galaxy clusters
  Nowadays, Machine Learning techniques offer fast and efficient solutions for
classification problems that would require intensive computational resources
via traditional methods. We examine the use of supervised Random Forest
algorithms to classify stars in simulated galaxy clusters, after subtracting
the stars belonging to the member galaxies. These dynamically different
components are interpreted as the individual properties of the stars in the
Brightest Cluster Galaxy (BCG) and IntraCluster Light (ICL). We employ matched
stellar catalogues (following, Dolag et al. 2010) of 29 simulated clusters from
the DIANOGA set to train and test the classifier. The input features are
cluster mass, normalized particle cluster-centric distance, and rest-frame
velocity. The model is found to correctly identify most of the star particles,
while the larger errors are exhibited at the BCG outskirt, where the
differences between the physical properties of the two components are less
obvious. We investigate the robustness of the classifier to numerical
resolution, redshift dependence (up to $z=1$), and included astrophysical
models. We claim that our classifier provides consistent results in simulations
at $z<1$, at different resolution levels and with significantly different
subgrid models. The phase-space structure is examined to assess whether the
general properties of the stellar components are recovered: indeed, BCG
outskirts represent the most critical region and are far more prone to
uncertainties in the classification process. In conclusion, this work suggests
the importance of employing Machine Learning to speed up a computationally
expensive classification in numerical simulations.
