Speaker
Roberto Ruiz de Austri
(IFIC, Valencia)
Description
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 provides 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.
Author
Roberto Ruiz de Austri
(IFIC, Valencia)