Search for an anomalous excess of charged-current quasi-elastic $ν_e$ interactions with the MicroBooNE experiment using Deep-Learning-based reconstruction

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Search for an anomalous excess of charged-current quasi-elastic $ν_e$ interactions with the MicroBooNE experiment using Deep-Learning-based reconstruction. / MicroBooNE Collaboration ; Blake, A.; Devitt, Alesha et al.
In: Physical Review D, Vol. 105, No. 11, 112003, 13.06.2022.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Bibtex

@article{5fb81aa601eb4e7a861de88dfc624258,

title = "Search for an anomalous excess of charged-current quasi-elastic $ν_e$ interactions with the MicroBooNE experiment using Deep-Learning-based reconstruction",

abstract = " We present a measurement of the $\nu_e$-interaction rate in the MicroBooNE detector that addresses the observed MiniBooNE anomalous low-energy excess (LEE). The approach taken isolates neutrino interactions consistent with the kinematics of charged-current quasi-elastic (CCQE) events. The topology of such signal events has a final state with 1 electron, 1 proton, and 0 mesons ($1e1p$). Multiple novel techniques are employed to identify a $1e1p$ final state, including particle identification that use two methods of deep-learning-based image identification, and event isolation using a boosted decision-tree ensemble trained to recognize two-body scattering kinematics. This analysis selects 25 $\nu_e$-candidate events in the reconstructed neutrino energy range of 200--1200\,MeV, while $29.0 \pm 1.9_\text{(sys)} \pm 5.4_\text{(stat)}$ are predicted when using $\nu_\mu$ CCQE interactions as a constraint. We use a simplified model to translate the MiniBooNE LEE observation into a prediction for a $\nu_e$ signal in MicroBooNE. A $\Delta \chi^2$ test statistic, based on the combined Neyman--Pearson $\chi^2$ formalism, is used to define frequentist confidence intervals for the LEE signal strength. Using this technique, in the case of no LEE signal, we expect this analysis to exclude a normalization factor of 0.75 (0.98) times the median MiniBooNE LEE signal strength at 90\% ($2\sigma$) confidence level, while the MicroBooNE data yield an exclusion of 0.25 (0.38) times the median MiniBooNE LEE signal strength at 90\% ($2\sigma$) confidence ",

keywords = "hep-ex",

author = "{MicroBooNE Collaboration} and P. Abratenko and R. An and J. Anthony and L. Arellano and J. Asaadi and A. Ashkenazi and S. Balasubramanian and B. Baller and G. Barr and V. Basque and L. Bathe-Peters and Rodrigues, {O. Benevides} and S. Berkman and A. Bhanderi and A. Bhat and M. Bishai and A. Blake and T. Bolton and Book, {J. Y.} and L. Camilleri and D. Caratelli and Terrazas, {I. Caro} and F. Cavanna and G. Cerati and D. Cianci and Collin, {G. H.} and Conrad, {J. M.} and M. Convery and L. Cooper-Troendle and Crespo-Anadon, {J. I.} and Alesha Devitt and J. Nowak and N. Patel and C. Thorpe",

note = "{\textcopyright} 2022 American Physical Society ",

year = "2022",

month = jun,

day = "13",

doi = "10.1103/PhysRevD.105.112003",

language = "English",

volume = "105",

journal = "Physical Review D",

issn = "1550-7998",

publisher = "American Physical Society",

number = "11",

}

RIS

TY - JOUR

T1 - Search for an anomalous excess of charged-current quasi-elastic $ν_e$ interactions with the MicroBooNE experiment using Deep-Learning-based reconstruction

AU - MicroBooNE Collaboration

AU - Abratenko, P.

AU - An, R.

AU - Anthony, J.

AU - Arellano, L.

AU - Asaadi, J.

AU - Ashkenazi, A.

AU - Balasubramanian, S.

AU - Baller, B.

AU - Barr, G.

AU - Basque, V.

AU - Bathe-Peters, L.

AU - Rodrigues, O. Benevides

AU - Berkman, S.

AU - Bhanderi, A.

AU - Bhat, A.

AU - Bishai, M.

AU - Blake, A.

AU - Bolton, T.

AU - Book, J. Y.

AU - Camilleri, L.

AU - Caratelli, D.

AU - Terrazas, I. Caro

AU - Cavanna, F.

AU - Cerati, G.

AU - Cianci, D.

AU - Collin, G. H.

AU - Conrad, J. M.

AU - Convery, M.

AU - Cooper-Troendle, L.

AU - Crespo-Anadon, J. I.

AU - Devitt, Alesha

AU - Nowak, J.

AU - Patel, N.

AU - Thorpe, C.

PY - 2022/6/13

Y1 - 2022/6/13

N2 - We present a measurement of the $\nu_e$-interaction rate in the MicroBooNE detector that addresses the observed MiniBooNE anomalous low-energy excess (LEE). The approach taken isolates neutrino interactions consistent with the kinematics of charged-current quasi-elastic (CCQE) events. The topology of such signal events has a final state with 1 electron, 1 proton, and 0 mesons ($1e1p$). Multiple novel techniques are employed to identify a $1e1p$ final state, including particle identification that use two methods of deep-learning-based image identification, and event isolation using a boosted decision-tree ensemble trained to recognize two-body scattering kinematics. This analysis selects 25 $\nu_e$-candidate events in the reconstructed neutrino energy range of 200--1200\,MeV, while $29.0 \pm 1.9_\text{(sys)} \pm 5.4_\text{(stat)}$ are predicted when using $\nu_\mu$ CCQE interactions as a constraint. We use a simplified model to translate the MiniBooNE LEE observation into a prediction for a $\nu_e$ signal in MicroBooNE. A $\Delta \chi^2$ test statistic, based on the combined Neyman--Pearson $\chi^2$ formalism, is used to define frequentist confidence intervals for the LEE signal strength. Using this technique, in the case of no LEE signal, we expect this analysis to exclude a normalization factor of 0.75 (0.98) times the median MiniBooNE LEE signal strength at 90\% ($2\sigma$) confidence level, while the MicroBooNE data yield an exclusion of 0.25 (0.38) times the median MiniBooNE LEE signal strength at 90\% ($2\sigma$) confidence

AB - We present a measurement of the $\nu_e$-interaction rate in the MicroBooNE detector that addresses the observed MiniBooNE anomalous low-energy excess (LEE). The approach taken isolates neutrino interactions consistent with the kinematics of charged-current quasi-elastic (CCQE) events. The topology of such signal events has a final state with 1 electron, 1 proton, and 0 mesons ($1e1p$). Multiple novel techniques are employed to identify a $1e1p$ final state, including particle identification that use two methods of deep-learning-based image identification, and event isolation using a boosted decision-tree ensemble trained to recognize two-body scattering kinematics. This analysis selects 25 $\nu_e$-candidate events in the reconstructed neutrino energy range of 200--1200\,MeV, while $29.0 \pm 1.9_\text{(sys)} \pm 5.4_\text{(stat)}$ are predicted when using $\nu_\mu$ CCQE interactions as a constraint. We use a simplified model to translate the MiniBooNE LEE observation into a prediction for a $\nu_e$ signal in MicroBooNE. A $\Delta \chi^2$ test statistic, based on the combined Neyman--Pearson $\chi^2$ formalism, is used to define frequentist confidence intervals for the LEE signal strength. Using this technique, in the case of no LEE signal, we expect this analysis to exclude a normalization factor of 0.75 (0.98) times the median MiniBooNE LEE signal strength at 90\% ($2\sigma$) confidence level, while the MicroBooNE data yield an exclusion of 0.25 (0.38) times the median MiniBooNE LEE signal strength at 90\% ($2\sigma$) confidence

KW - hep-ex

U2 - 10.1103/PhysRevD.105.112003

DO - 10.1103/PhysRevD.105.112003

M3 - Journal article

VL - 105

JO - Physical Review D

JF - Physical Review D

SN - 1550-7998

IS - 11

M1 - 112003

ER -

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