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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Improving neutrino energy estimation of charged-current interaction events with recurrent neural networks in MicroBooNE
AU - MicroBooNE Collaboration
AU - Abratenko, P.
AU - Alterkait, O.
AU - Aldana, D. Andrade
AU - Arellano, L.
AU - Asaadi, J.
AU - Ashkenazi, A.
AU - Balasubramanian, S.
AU - Baller, B.
AU - Barnard, A.
AU - Barr, G.
AU - Barrow, D.
AU - Barrow, J.
AU - Basque, V.
AU - Bateman, J.
AU - Rodrigues, O. Benevides
AU - Berkman, S.
AU - Bhanderi, A.
AU - Bhat, A.
AU - Bhattacharya, M.
AU - Bishai, M.
AU - Blake, A.
AU - Bogart, B.
AU - Bolton, T.
AU - Book, J. Y.
AU - Brunetti, M. B.
AU - Camilleri, L.
AU - Cao, Y.
AU - Caratelli, D.
AU - Cavanna, F.
AU - Cerati, G.
AU - Chappell, A.
AU - Chen, Y.
AU - Conrad, J. M.
AU - Convery, M.
AU - Cooper-Troendle, L.
AU - Crespo-Anadon, J. I.
AU - Cross, R.
AU - Tutto, M. Del
AU - Dennis, S. R.
AU - Detje, P.
AU - Diurba, R.
AU - Djurcic, Z.
AU - Dorrill, R.
AU - Duffy, K.
AU - Gu, L.
AU - Mawby, I.
AU - Nowak, J.
AU - Patel, N.
AU - Pophale, I.
PY - 2024/11/1
Y1 - 2024/11/1
N2 - We present a deep learning-based method for estimating the neutrino energy of charged-current neutrino-argon interactions. We employ a recurrent neural network (RNN) architecture for neutrino energy estimation in the MicroBooNE experiment, utilizing liquid argon time projection chamber (LArTPC) detector technology. Traditional energy estimation approaches in LArTPCs, which largely rely on reconstructing and summing visible energies, often experience sizable biases and resolution smearing because of the complex nature of neutrino interactions and the detector response. The estimation of neutrino energy can be improved after considering the kinematics information of reconstructed final-state particles. Utilizing kinematic information of reconstructed particles, the deep learning-based approach shows improved resolution and reduced bias for the muon neutrino Monte Carlo simulation sample compared to the traditional approach. In order to address the common concern about the effectiveness of this method on experimental data, the RNN-based energy estimator is further examined and validated with dedicated data-simulation consistency tests using MicroBooNE data. We also assess its potential impact on a neutrino oscillation study after accounting for all statistical and systematic uncertainties and show that it enhances physics sensitivity. This method has good potential to improve the performance of other physics analyses.
AB - We present a deep learning-based method for estimating the neutrino energy of charged-current neutrino-argon interactions. We employ a recurrent neural network (RNN) architecture for neutrino energy estimation in the MicroBooNE experiment, utilizing liquid argon time projection chamber (LArTPC) detector technology. Traditional energy estimation approaches in LArTPCs, which largely rely on reconstructing and summing visible energies, often experience sizable biases and resolution smearing because of the complex nature of neutrino interactions and the detector response. The estimation of neutrino energy can be improved after considering the kinematics information of reconstructed final-state particles. Utilizing kinematic information of reconstructed particles, the deep learning-based approach shows improved resolution and reduced bias for the muon neutrino Monte Carlo simulation sample compared to the traditional approach. In order to address the common concern about the effectiveness of this method on experimental data, the RNN-based energy estimator is further examined and validated with dedicated data-simulation consistency tests using MicroBooNE data. We also assess its potential impact on a neutrino oscillation study after accounting for all statistical and systematic uncertainties and show that it enhances physics sensitivity. This method has good potential to improve the performance of other physics analyses.
KW - hep-ex
KW - physics.ins-det
U2 - 10.1103/PhysRevD.110.092010
DO - 10.1103/PhysRevD.110.092010
M3 - Journal article
VL - 110
JO - Physical Review D
JF - Physical Review D
SN - 1550-7998
IS - 9
M1 - 092010
ER -