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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Combining charge and light signals in LArTPCs yields better sub-GeV energy reconstruction, 70% electron neutrino-antineutrino separation efficiency, and about 20-degree direction improvement for antineutrinos via neutron isolation algorithms.
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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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Enhanced Reconstruction of Sub-GeV Neutrinos Charged Current Interactions in LArTPC
Combining charge and light signals in LArTPCs yields better sub-GeV energy reconstruction, 70% electron neutrino-antineutrino separation efficiency, and about 20-degree direction improvement for antineutrinos via neutron isolation algorithms.