[HYBRID] SPACE: High-Performance Computing (HPC) Visualization

Session

Generalizing ICL Predictions Across Simulations: A Deep Learning Approach by Marta Barroso isidoro and Pablo Agustin Martin Torres (BSC)

14 May 2025, 15:30

Barcelona Supercomputing Center (Online and Onsite)

Description

We develop a machine learning framework to infer intracluster light (ICL) properties from velocity dispersion maps in simulated galaxy clusters, using deep learning models trained on mock images from multiple hydrodynamical simulations, including DIANOGA, Illustris, Magneticum, MillenniumTNG, and FLAMINGO. By leveraging synthetic data with projection variations, our approach aims to generalize across different simulation environments without dependence on specific physical models. This work presents a simulation-independent method for studying ICL, bridging kinematic and morphological information to provide new insights into its formation and evolution.