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DTSTART;TZID=UTC:20241031T160000
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CREATED:20241002T173737Z
LAST-MODIFIED:20241002T173737Z
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SUMMARY:Speaker: Vinicius Mikuni (LBNL) - Title: OmniLearn: A Method to Simultaneously Facilitate All Jet Physics Tasks
DESCRIPTION:Research Progress Meeting \nDate: October 31\, 2024 \nTime: 4:00- 5:00 pm \nLocation: Sessler Conference Room- 50A-5132 [In-Person and HYBRID]  \nSpeaker: Vinicius Mikuni (LBNL) \nTitle: OmniLearn: A Method to Simultaneously Facilitate All Jet Physics Tasks \nAbstract: Machine learning has become an essential tool in the study of jets\, collimated sprays of particles emerging from strong force interactions. Due to their complex\, high-dimensional nature\, jets can be explored holistically by neural networks in ways that are not possible manually. However\, innovations in all areas of jet physics are proceeding in parallel. We show that specially constructed machine learning models trained for a specific jet classification task can improve the accuracy\, precision\, or speed of all other jet physics tasks. This is demonstrated by training on a particular multiclass generation and classification task and then using the learned representation for different generation and classification tasks\, for datasets with a different (full) detector simulation\, for jets from a different collision system (pp versus ep)\, for generative models\, for likelihood ratio estimation\, and for anomaly detection. Our OmniLearn approach is thus a foundation model and is made publicly available for use in any area where state-of-the-art precision is required for analyses involving jets and their substructure. \nJoin Zoom Meeting \nhttps://lbnl.zoom.us/j/98854322464?pwd=K2tKUm1VZjRlV1J5RHE3cXdHQzRxdz09\n\nMeeting ID: 988 5432 2464\n\nPasscode: 142239
URL:https://rpm.physics.lbl.gov/event/speaker-vinicius-mikuni-lbnl-title-omnilearn-a-method-to-simultaneously-facilitate-all-jet-physics-tasks/
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