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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In simulated sub-GeV neutrino-argon events, combined charge+light calorimetry gives the best energy, an SVM on charge/light features separates νe from anti-νe at ~70%, and nearest-deposit neutron tagging improves antineutrino direction by ~20°.
citing papers explorer
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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
In simulated sub-GeV neutrino-argon events, combined charge+light calorimetry gives the best energy, an SVM on charge/light features separates νe from anti-νe at ~70%, and nearest-deposit neutron tagging improves antineutrino direction by ~20°.