Electromagnetic Shower Reconstruction and Energy Validation with Michel Electrons and $π^0$ Samples for the Deep-Learning-Based Analyses in MicroBooNE

, MicroBooNE Collaboration and Blake, A. and Devitt, A. and Nowak, J. and Patel, N. and Thorpe, C. (2021) Electromagnetic Shower Reconstruction and Energy Validation with Michel Electrons and $π^0$ Samples for the Deep-Learning-Based Analyses in MicroBooNE. Journal of Instrumentation. ISSN 1748-0221 (In Press)

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Abstract

This article presents the reconstruction of the electromagnetic activity from electrons and photons (showers) used in the MicroBooNE deep learning-based low energy electron search. The reconstruction algorithm uses a combination of traditional and deep learning-based techniques to estimate shower energies. We validate these predictions using two $\nu_{\mu}$-sourced data samples: charged/neutral current interactions with final state neutral pions and charged current interactions in which the muon stops and decays within the detector producing a Michel electron. Both the neutral pion sample and Michel electron sample demonstrate agreement between data and simulation. Further, the absolute shower energy scale is shown to be consistent with the relevant physical constant of each sample: the neutral pion mass peak and the Michel energy cutoff.

Item Type:
Journal Article
Journal or Publication Title:
Journal of Instrumentation
Additional Information:
27 pages
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2600/2610
Subjects:
ID Code:
163599
Deposited By:
Deposited On:
17 Dec 2021 17:45
Refereed?:
Yes
Published?:
In Press
Last Modified:
10 Jan 2022 09:50