Improving neutrino energy estimation of charged-current interaction events with recurrent neural networks in MicroBooNE
Physical Review D American Physical Society (APS) 110:9 (2024) 092010
Demonstration of neutron identification in neutrino interactions in the MicroBooNE liquid argon time projection chamber
The European Physical Journal C SpringerOpen 84:10 (2024) 1052
Abstract:
A significant challenge in measurements of neutrino oscillations is reconstructing the incoming neutrino energies. While modern fully-active tracking calorimeters such as liquid argon time projection chambers in principle allow the measurement of all final state particles above some detection threshold, undetected neutrons remain a considerable source of missing energy with little to no data constraining their production rates and kinematics. We present the first demonstration of tagging neutrino-induced neutrons in liquid argon time projection chambers using secondary protons emitted from neutron-argon interactions in the MicroBooNE detector. We describe the method developed to identify neutrino-induced neutrons and demonstrate its performance using neutrons produced in muon-neutrino charged current interactions. The method is validated using a small subset of MicroBooNE’s total dataset. The selection yields a sample with 60% of selected tracks corresponding to neutron-induced secondary protons. At this purity, the integrated efficiency is 8.4% for neutrons that produce a detectable proton.First double-differential cross section measurement of neutral-current $π^0$ production in neutrino-argon scattering in the MicroBooNE detector
(2024)