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SUMMARY:Speaker: Nicole Hartman (Technical University of Munich) - Title: b-tagging unlocks the Higgs Potential — deep learning for jet flavour tagging on ATLAS
DESCRIPTION:Research Progress Meeting \nDate: July 3\, 2025 \nTime: noon – 1:00 pm [note special time] \nLocation: Sessler Conference Room- 50A-5132 [In-Person and HYBRID]  \nSpeaker: Nicole Hartman (Technical University of Munich) \nTitle: b-tagging unlocks the Higgs Potential — deep learning for jet flavour tagging on ATLAS \nAbstract: \nElectroweak symmetry breaking involves a Higgs potential that generates mass for the weak bosons. In the Standard Model\, this potential has a quartic form—but this is merely an ansatz that has yet to be experimentally verified. A first experimental probe of the Higgs potential could come from measuring the simultaneous production of two Higgs bosons (“di-Higgs”). This is an exceptionally rare process\, expected to require another decade of data collection to discover using current analysis methods. The most probable decay mode of a Higgs boson is into a pair of b-quarks\, which in turn produce b-jets in the detector. The channels driving di-Higgs sensitivity therefore all include b-jets in the final state. \nMachine learning is transforming many areas of life\, and particle physics is no exception. Because of the complexity of jets at the LHC\, b-jet classification (or flavour tagging) has become a leading application of deep learning in the field. This seminar presents the state-of-the-art in flavour tagging: a transformer model\, the same backbone architecture used in ChatGPT. These modern transformers deliver an impressive factor-of-four improvement in performance compared to earlier b-taggers based on recurrent neural networks. In addition\, transformers are more data-efficient\, allowing us to benefit from a tenfold increase in training statistics. Importantly\, these gains in simulation also translate to actual LHC data. \nTransformers are general-purpose architectures that can be integrated at multiple stages of the analysis pipeline. As training datasets continue to grow\, we can begin to view jet tagging as a foundation model for LHC physics—one that can be customized or “fine-tuned” for specific physics goals. We conclude by highlighting how an end-to-end optimizable analysis can fine-tune a jet-tagger for an HH physics search\, helping to further advance our di-Higgs program and quest to understand the Higgs potential. \n  \nJoin Zoom Meeting\nhttps://lbnl.zoom.us/j/98881011162?pwd=1kC2cSwdAjvJJUer0ymPMklwe8NFOE.1\n \nMeeting ID: 988 8101 1162 \nPasscode: 696672
URL:https://rpm.physics.lbl.gov/event/speaker-nicole-hartman-technical-university-of-munich-title-tba/
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