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Machine Learning for the LHCb Simulation
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Machine Learning for the LHCb Simulation
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Most of the computing resources pledged to the LHCb experiment at CERN are necessary to the production of simulated samples used to predict resolution functions on the reconstructed quantities and the reconstruction and selection efficiency. Projecting the Simulation requests to the years following the upcoming LHCb Upgrade, the relative computing resources would exceed the pledges by more than a factor of 2. In this contribution, I discuss how Machine Learning can help to speed up the Detector Simulation for the upcoming Runs of the LHCb experiment.
Forward citations
Cited by 2 Pith papers
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Local Conformal Predictions for Calibrated Surrogates
FALCON is a novel conformal prediction technique that learns locally calibrated confidence intervals for neural network surrogates modeling LHC scattering amplitudes.
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Verification of ML in fundamental physics is essential precisely when models enter statistical modeling, inference, or hypothesis testing, and is bounded by unavoidable inductive bias, sample complexity, and experimen...
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