Search for an anomalous excess of charged-current quasielastic νe interactions with the MicroBooNE experiment using Deep-Learning-based reconstruction
We present a measurement of the ν 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 quasielastic (CCQE) events. The topology of s...
Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Other Authors: | |
Format: | Journal article |
Language: | English |
Published: |
American Physical Society
2022
|
Summary: | We present a measurement of the
ν
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 quasielastic (CCQE) events. The topology of such signal events has a final state with one electron, one proton, and zero mesons (
1
e
1
p
). Multiple novel techniques are employed to identify a
1
e
1
p
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
ν
e
-candidate events in the reconstructed neutrino energy range of 200–1200 MeV, while
29.0
±
1.
9
(
sys
)
±
5.
4
(
stat
)
are predicted when using
ν
μ
CCQE interactions as a constraint. We use a simplified model to translate the MiniBooNE LEE observation into a prediction for a
ν
e
signal in MicroBooNE. A
Δ
χ
2
test statistic, based on the combined Neyman–Pearson
χ
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
σ
) confidence level, while the MicroBooNE data yield an exclusion of 0.25 (0.38) times the median MiniBooNE LEE signal strength at 90% (
2
σ
) confidence level. |
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