Researchers describe AgentRivet, a system that automatically generates Rivet analysis routines from published journal articles. Rivet is a C++ toolkit used in collider physics to compare new theoretical models against preserved experimental measurements. The work aims to reduce the manual effort of encoding published analyses into reusable software.
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.
A new research paper explores applying reinforcement learning to searches for new physics in particle physics, focusing on anomalies where low-energy measurements deviate from Standard Model predictions. The work targets one of the field's most important open problems: identifying evidence of physics beyond the Standard Model.