Kavli Affiliate: Michael McDonald
| First 5 Authors: Maria Sadikov, Julie Hlavacek-Larrondo, Laurence Perreault Levasseur, Carter Lee Rhea, Michael McDonald
| Summary:
We present an analysis of the X-ray properties of the galaxy cluster
population in the z=0 snapshot of the IllustrisTNG simulations, utilizing
machine learning techniques to perform clustering and regression tasks. We
examine five properties of the hot gas (the central cooling time, the central
electron density, the central entropy excess, the concentration parameter, and
the cuspiness) which are commonly used as classification metrics to identify
cool core (CC), weak cool core (WCC) and non cool core (NCC) clusters of
galaxies. Using mock Chandra X-ray images as inputs, we first explore an
unsupervised clustering scheme to see how the resulting groups correlate with
the CC/WCC/NCC classification based on the different criteria. We observe that
the groups replicate almost exactly the separation of the galaxy cluster images
when classifying them based on the concentration parameter. We then move on to
a regression task, utilizing a ResNet model to predict the value of all five
properties. The network is able to achieve a mean percentage error of 1.8% for
the central cooling time, and a balanced accuracy of 0.83 on the concentration
parameter, making them the best-performing metrics. Finally, we use
simulation-based inference (SBI) to extract posterior distributions for the
network predictions. Our neural network simultaneously predicts all five
classification metrics using only mock Chandra X-ray images.
This study demonstrates that machine learning is a viable approach for
analyzing and classifying the large galaxy cluster datasets that will soon
become available through current and upcoming X-ray surveys, such as eROSITA.
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