Bridging observations and simulations: a machine learning approach to galaxy clusters

Abstract

The intracluster medium (ICM) records the history of galaxy clusters through its complex dynamical properties. To effectively interpret these properties, robust methods are needed to compare observational data with theoretical models. We present a novel machine learning framework for comparing ICM line-of-sight velocity maps derived from X-ray observations. Our approach uses convolutional and Siamese neural networks to identify similarities between different kinematic fields. We outline the architecture of this framework and perform a series of sanity checks to validate its performance. These checks demonstrate the model's ability to correctly identify and quantify kinematic features, establishing a powerful new tool for future comparative studies of the ICM.