Dr. Roberto Ruiz de Austri Bazan: Deep Learning in the Search for Dark Matter: An Overview

Roberto Ruiz de Austri is a researcher at the Spanish National Research Council (CSIC) and the Institute of Corpuscular Physics (IFIC), working on physics Beyond the Standard Model, with a focus on applying artificial intelligence to Dark Matter (DM) searches from both particle and astroparticle physics perspectives, as well as research in cosmology and gravitational-wave physics. He is a core member of GAMBIT collaboration and contributes to CheckMAT and CosmiXs, with involvement in the ATLAS and MoEDAL experiments and the LISA mission. He co-founded the initiative for DM searches using AI (DarkMachines), contributed to the creation of the European Initiative for Advancing AI in Fundamental Physics (EuCAIF), and serves in the scientific governance of the Machine-learning Optimized Design of Experiments (MODE) collaboration.

Abstract: Deep learning has quickly become a valuable tool in the quest to understand dark matter, helping researchers explore faint signals across a range of experiments, from high-energy colliders to direct and indirect searches. By sifting through vast datasets and uncovering subtle patterns, these techniques can reveal signs of dark matter that traditional approaches might miss. This talk will provide a  broad overview of how deep learning and related machine learning methods are reshaping our search for dark matter, with an emphasis on key breakthroughs and ongoing challenges. We will also look ahead to promising future directions.