28 September 2026 to 2 October 2026
University of Oxford
Europe/London timezone

"Hadron-in-fat-jet'' AI Tagging to Detect Rare Decays

Not scheduled
20m
University of Oxford

University of Oxford

Standard slot New techniques, tools, and generators New techniques, tools and generators

Speakers

Mr Linrui Chen (Peking University) Qiang Li (Peking University (CN)) Youpeng Wu (Peking University (CN))

Description

We investigate a novel class of boosted-object signatures at the LHC, where a high-pT fat-jet contains an identifiable hadron or quarkonium state originating from rare or semi-exclusive decays. Unlike conventional boosted jet studies, which focus on multi-prong partonic substructure, our approach probes hybrid configurations such as W±→π±γ, where a localized hadronic or quarkonium signal is embedded within a collimated jet. By fine-tuning the signature-oriented, pre-trained Sophon AI model optimized for large-radius jets, and combining it with an event-level BDT and a soft-drop-mass shape fit, we obtain an expected 95\% CL upper limit of (W±→π±γ)<2.78×10−5 for 450fb−1 in our nominal setup. This study serves as a first proof-of-principle demonstration of the ``hadron-in-fat-jet'' paradigm; substantial gains in sensitivity are expected from improved trigger strategies, additional production channels, and dedicated taggers, while the methodology itself is broadly applicable to a wide range of rare Standard Model processes and searches for light or exotic resonances at present and future collider experiments. https://arxiv.org/abs/2606.09458

Authors

Mr Linrui Chen (Peking University) Qiang Li (Peking University (CN)) Youpeng Wu (Peking University (CN))

Presentation materials

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