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...
| Main Authors: | , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2022
|
| Online Access: | https://hdl.handle.net/1721.1/142006 |
| _version_ | 1811072742604144640 |
|---|---|
| author | Hen, Or Conrad, Janet |
| author2 | Massachusetts Institute of Technology. Department of Physics |
| author_facet | Massachusetts Institute of Technology. Department of Physics Hen, Or Conrad, Janet |
| author_sort | Hen, Or |
| collection | MIT |
| description | <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> |
| first_indexed | 2024-09-23T09:11:02Z |
| format | Article |
| id | mit-1721.1/142006 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T09:11:02Z |
| publishDate | 2022 |
| publisher | IOP Publishing |
| record_format | dspace |
| spelling | mit-1721.1/1420062023-03-28T20:10:21Z Electromagnetic shower reconstruction and energy validation with Michel electrons and π 0 samples for the deep-learning-based analyses in MicroBooNE Hen, Or Conrad, Janet Massachusetts Institute of Technology. Department of Physics <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> 2022-04-21T15:14:43Z 2022-04-21T15:14:43Z 2021 2022-04-21T15:01:40Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/142006 Hen, Or and Conrad, Janet. 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, 16 (12). en 10.1088/1748-0221/16/12/T12017 Journal of Instrumentation Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf IOP Publishing arXiv |
| spellingShingle | Hen, Or Conrad, Janet Electromagnetic shower reconstruction and energy validation with Michel electrons and π 0 samples for the deep-learning-based analyses in MicroBooNE |
| title | Electromagnetic shower reconstruction and energy validation with Michel electrons and π 0 samples for the deep-learning-based analyses in MicroBooNE |
| title_full | Electromagnetic shower reconstruction and energy validation with Michel electrons and π 0 samples for the deep-learning-based analyses in MicroBooNE |
| title_fullStr | Electromagnetic shower reconstruction and energy validation with Michel electrons and π 0 samples for the deep-learning-based analyses in MicroBooNE |
| title_full_unstemmed | Electromagnetic shower reconstruction and energy validation with Michel electrons and π 0 samples for the deep-learning-based analyses in MicroBooNE |
| title_short | Electromagnetic shower reconstruction and energy validation with Michel electrons and π 0 samples for the deep-learning-based analyses in MicroBooNE |
| title_sort | electromagnetic shower reconstruction and energy validation with michel electrons and π 0 samples for the deep learning based analyses in microboone |
| url | https://hdl.handle.net/1721.1/142006 |
| work_keys_str_mv | AT henor electromagneticshowerreconstructionandenergyvalidationwithmichelelectronsandp0samplesforthedeeplearningbasedanalysesinmicroboone AT conradjanet electromagneticshowerreconstructionandenergyvalidationwithmichelelectronsandp0samplesforthedeeplearningbasedanalysesinmicroboone |