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Novel machine learning applications at the LHC
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Novel machine learning applications at the LHC
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Machine learning (ML) is a rapidly growing area of research in the field of particle physics, with a vast array of applications at the CERN LHC. ML has changed the way particle physicists conduct searches and measurements as a versatile tool used to improve existing approaches and enable fundamentally new ones. In these proceedings, we describe novel ML techniques and recent results for improved classification, fast simulation, unfolding, and anomaly detection in LHC experiments.
Forward citations
Cited by 1 Pith paper
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HEP-CoPilot is a new multi-agent retrieval framework that retrieves, reconstructs, and compares experimental limits from HEP literature and HEPData to support interpretation of beyond-Standard-Model searches.
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