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Electromagnetic shower reconstruction and energy validation with Michel electrons and π0 samples for the deep-learning-based analyses in MicroBooNE
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The MicroBooNE Collaboration (Including: Marshall, John). (2021) Electromagnetic shower reconstruction and energy validation with Michel electrons and π0 samples for the deep-learning-based analyses in MicroBooNE. Journal of Instrumentation, 16 . T12017. doi:10.1088/1748-0221/16/12/T12017 ISSN 1748-0221.
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Official URL: https://doi.org/10.1088/1748-0221/16/12/T12017
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 νμ-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 | ||||||
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Physics | ||||||
Journal or Publication Title: | Journal of Instrumentation | ||||||
Publisher: | IOP Publishing Ltd | ||||||
ISSN: | 1748-0221 | ||||||
Official Date: | 21 December 2021 | ||||||
Dates: |
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Volume: | 16 | ||||||
Article Number: | T12017 | ||||||
DOI: | 10.1088/1748-0221/16/12/T12017 | ||||||
Status: | Peer Reviewed | ||||||
Publication Status: | Published | ||||||
Access rights to Published version: | Open Access (Creative Commons) |
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