Convolutional neural networks for shower energy prediction in liquid argon time projection chambers
<jats:title>Abstract</jats:title> <jats:p>When electrons with energies of O(100) MeV pass through a liquid argon time projection chamber (LArTPC), they deposit energy in the form of electromagnetic showers. Methods to reconstruct the energy of these shower...
| Main Authors: | , , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2022
|
| Online Access: | https://hdl.handle.net/1721.1/142032 |
| _version_ | 1826210638915960832 |
|---|---|
| author | Carloni, K Kamp, NW Schneider, A Conrad, JM |
| author2 | Massachusetts Institute of Technology. Department of Physics |
| author_facet | Massachusetts Institute of Technology. Department of Physics Carloni, K Kamp, NW Schneider, A Conrad, JM |
| author_sort | Carloni, K |
| collection | MIT |
| description | <jats:title>Abstract</jats:title>
<jats:p>When electrons with energies of O(100) MeV pass through a
liquid argon time projection chamber (LArTPC), they deposit energy
in the form of electromagnetic showers. Methods to reconstruct the
energy of these showers in LArTPCs often rely on the combination of
a clustering algorithm and a linear calibration between the shower
energy and charge contained in the cluster. This reconstruction
process could be improved through the use of a convolutional neural
network (CNN). Here we discuss the performance of various CNN-based
models on simulated LArTPC images, and then compare the best
performing models to a typical linear calibration algorithm. We
show that the CNN method is able to address inefficiencies caused by
unresponsive wires in LArTPCs and reconstruct a larger fraction of
imperfect events to within 5 % accuracy compared with the linear
algorithm.</jats:p> |
| first_indexed | 2024-09-23T14:52:49Z |
| format | Article |
| id | mit-1721.1/142032 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T14:52:49Z |
| publishDate | 2022 |
| publisher | IOP Publishing |
| record_format | dspace |
| spelling | mit-1721.1/1420322023-04-20T19:23:38Z Convolutional neural networks for shower energy prediction in liquid argon time projection chambers Carloni, K Kamp, NW Schneider, A Conrad, JM Massachusetts Institute of Technology. Department of Physics <jats:title>Abstract</jats:title> <jats:p>When electrons with energies of O(100) MeV pass through a liquid argon time projection chamber (LArTPC), they deposit energy in the form of electromagnetic showers. Methods to reconstruct the energy of these showers in LArTPCs often rely on the combination of a clustering algorithm and a linear calibration between the shower energy and charge contained in the cluster. This reconstruction process could be improved through the use of a convolutional neural network (CNN). Here we discuss the performance of various CNN-based models on simulated LArTPC images, and then compare the best performing models to a typical linear calibration algorithm. We show that the CNN method is able to address inefficiencies caused by unresponsive wires in LArTPCs and reconstruct a larger fraction of imperfect events to within 5 % accuracy compared with the linear algorithm.</jats:p> 2022-04-21T18:34:12Z 2022-04-21T18:34:12Z 2022-02-01 2022-04-21T18:29:27Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/142032 Carloni, K, Kamp, NW, Schneider, A and Conrad, JM. 2022. "Convolutional neural networks for shower energy prediction in liquid argon time projection chambers." Journal of Instrumentation, 17 (02). en 10.1088/1748-0221/17/02/p02022 Journal of Instrumentation Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf IOP Publishing arXiv |
| spellingShingle | Carloni, K Kamp, NW Schneider, A Conrad, JM Convolutional neural networks for shower energy prediction in liquid argon time projection chambers |
| title | Convolutional neural networks for shower energy prediction in liquid argon time projection chambers |
| title_full | Convolutional neural networks for shower energy prediction in liquid argon time projection chambers |
| title_fullStr | Convolutional neural networks for shower energy prediction in liquid argon time projection chambers |
| title_full_unstemmed | Convolutional neural networks for shower energy prediction in liquid argon time projection chambers |
| title_short | Convolutional neural networks for shower energy prediction in liquid argon time projection chambers |
| title_sort | convolutional neural networks for shower energy prediction in liquid argon time projection chambers |
| url | https://hdl.handle.net/1721.1/142032 |
| work_keys_str_mv | AT carlonik convolutionalneuralnetworksforshowerenergypredictioninliquidargontimeprojectionchambers AT kampnw convolutionalneuralnetworksforshowerenergypredictioninliquidargontimeprojectionchambers AT schneidera convolutionalneuralnetworksforshowerenergypredictioninliquidargontimeprojectionchambers AT conradjm convolutionalneuralnetworksforshowerenergypredictioninliquidargontimeprojectionchambers |