Speakers
Description
Context. Intracluster light (ICL) is a key tracer of the dynamical history of galaxy clusters, yet its extraction from simulations remains challenging. Velocity dispersion, which reflects the effects of mergers and interactions, provides an alternative approach for estimating ICL distribution.
Aims. We aim to develop a generalizable machine learning framework to infer ICL properties from velocity dispersion maps in simulated galaxy clusters, leveraging a diverse set of hydrodynamical simulations. Our method seeks to identify ICL across different environments without relying on a specific simulation’s physical model, thus enhancing its applicability.
Methods. Using deep learning techniques, we train different types of neural network models (such as UNet, AttentionUNet, Mamba, etc) on mock images generated from a set of simulations that includes DIANOGA, Illustris, Magneticum, MilleniumTNG and Flamingo. Our dataset consists of synthetic velocity dispersion maps and corresponding ICL labels, augmented through projection variations and spatial shifts. The model is designed to extract complex spatial correlations between velocity dispersion and ICL morphology, enabling robust predictions across different simulation datasets.
Results. Our findings demonstrate that the trained model successfully reconstructs ICL maps from velocity dispersion images, with good generalization between DIANOGA and Illustris.
Conclusions. This work provides a framework for inferring ICL from velocity dispersion, offering a simulation-independent approach that can be applied to a wide range of datasets. By bridging kinematic information with ICL morphology, our method lays the groundwork for new insights into the formation and evolution of ICL in galaxy clusters.