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invisible particles

topic1 events
papersSEP 10 04:00 UTC

Self-supervised learning maps heavy-flavour decays at LHCb

A new preprint applies self-supervised learning to build representations of beauty and charm hadron decays in LHCb data. These decays serve as sensitive probes of physics beyond the standard model, including channels with invisible particles that leave no detector signature. The technique is designed to extract structure from the collider's large heavy-flavour datasets.

self-supervised/particle-physics/research/machine-learning