Speaker
Description
Jet tagging, identifying the origin of jets produced in particle collisions, is a critical classification task in high-energy physics. Despite the revolutionary impact of deep learning on jet tagging over the past decade, the paradigm has remained unchanged. In particular, jets within the same collision event are classified independently, one at a time. This single-jet approach ignores correlations, overlaps, and wider event context between jets that can greatly improve classification performance. We introduce PanopTag, a new paradigm for jet tagging that simultaneously tags all jets by employing an encoder-decoder architecture that uses jet kinematics as queries to cross-attend to particle flow object embeddings from the full collision event. We evaluate PanopTag on heavy-flavor $(b/c)$-tagging and demonstrate remarkable performance improvements over state-of-the-art single-jet baselines that are only accessible by exploiting event-level
features and correlations between jets. We demonstrate the absence of topology bias and conditional independence of jet flavor predictions, showing that PanopTag could utilize established calibration strategies for ultimate deployment in collider data analysis.