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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.
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Cited by 2 Pith papers
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From Qubits to Couplings: A Hybrid Quantum Machine Learning Framework for LHC Physics
A hybrid quantum-classical classifier on simulated HH→bbγγ events claims 95% CL limits of 1.9–2.1×SM, but the gain over XGBoost is 21–29%, not the advertised factor of two.
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SuperSONIC: Cloud-Native Infrastructure for ML Inferencing
SuperSONIC is a cloud-native inference-as-a-service framework for scientific experiments, and its automatic GPU scaling improves average latency and GPU utilization over static allocations in a synthetic test.
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