TY - UNPB

T1 - νm​u Disappearance in MicroBooNE using the Deep Learning 1μ1p Selection

AU - MicroBooNE Collaboration

AU - Nowak, Jaroslaw

PY - 2022/5/30

Y1 - 2022/5/30

N2 - We test a 3+1 model with the MicroBooNE data using a 1µ1p selection developed using Deep-Learning-based reconstruction. In order to test this model we apply a muon neutrino disappearance effect to the selection, and search across a grid of oscillation model parameters using a Feldman Cousins technique. We determine MicroBooNE’s sensitivity across this model parameter space, and perform several validation studies to test this study’s robustness. Finally, we examine the allowed and excluded regions per MicroBooNE’s data at 90% confidence, using a data set corresponding to 6.67 × 1020 protons on target. The null model remains allowed, and several of the high-disappearance models are excluded.

AB - We test a 3+1 model with the MicroBooNE data using a 1µ1p selection developed using Deep-Learning-based reconstruction. In order to test this model we apply a muon neutrino disappearance effect to the selection, and search across a grid of oscillation model parameters using a Feldman Cousins technique. We determine MicroBooNE’s sensitivity across this model parameter space, and perform several validation studies to test this study’s robustness. Finally, we examine the allowed and excluded regions per MicroBooNE’s data at 90% confidence, using a data set corresponding to 6.67 × 1020 protons on target. The null model remains allowed, and several of the high-disappearance models are excluded.

U2 - 10.2172/2406202

DO - 10.2172/2406202

M3 - Preprint

BT - νm​u Disappearance in MicroBooNE using the Deep Learning 1μ1p Selection

ER -