PanopTag: Simultaneously Tagging All Jets in a Particle Collision Event
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| Format: | Preprint |
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2026
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| _version_ | 1866917219360833536 |
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| author | Qureshi, Umar Sohail Bullard, Brendon Schwartzman, Ariel |
| author_facet | Qureshi, Umar Sohail Bullard, Brendon Schwartzman, Ariel |
| contents | 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 are classified independently, one at a time. This single-jet approach ignores correlations, overlaps, and wider event context between jets. We introduce PanopTag, a new paradigm for jet tagging that departs from traditional single-jet tagging approaches. Rather than classifying jets independently, PanopTag simultaneously tags all jets by employing an encoder-decoder architecture that uses jet kinematics as queries to cross-attend to particle flow object embeddings. 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2601_16417 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | PanopTag: Simultaneously Tagging All Jets in a Particle Collision Event Qureshi, Umar Sohail Bullard, Brendon Schwartzman, Ariel High Energy Physics - Phenomenology Data Analysis, Statistics and Probability 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 are classified independently, one at a time. This single-jet approach ignores correlations, overlaps, and wider event context between jets. We introduce PanopTag, a new paradigm for jet tagging that departs from traditional single-jet tagging approaches. Rather than classifying jets independently, PanopTag simultaneously tags all jets by employing an encoder-decoder architecture that uses jet kinematics as queries to cross-attend to particle flow object embeddings. 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. |
| title | PanopTag: Simultaneously Tagging All Jets in a Particle Collision Event |
| topic | High Energy Physics - Phenomenology Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2601.16417 |