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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Supervised ML classification of neutrino events by interaction channel prior to energy reconstruction improves accuracy and sensitivity by 10-20% in simulated DUNE analyses while remaining robust to generator mismodeling.
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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.
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Improving Neutrino Oscillation Measurements through Event Classification
Supervised ML classification of neutrino events by interaction channel prior to energy reconstruction improves accuracy and sensitivity by 10-20% in simulated DUNE analyses while remaining robust to generator mismodeling.