Electromagnetic shower reconstruction and energy validation with Michel electrons and π 0 samples for the deep-learning-based analyses in MicroBooNE
<jats:title>Abstract</jats:title> <jats:p>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...
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2022
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| Online Access: | https://hdl.handle.net/1721.1/142006 |
| Summary: | <jats:title>Abstract</jats:title>
<jats:p>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
ν<jats:sub>μ</jats:sub>-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.</jats:p> |
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