Convolutional neural networks for shower energy prediction in liquid argon time projection chambers
Name
2110.10766.pdf
Description
Accepted version
Size
1.44 MB
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Adobe PDF
Checksum (MD5)
ba76b41bc8e7ac0170cf01b09bd4a7bf
Author(s)
Carloni, K
Kamp, NW
Schneider, A
Conrad, JM
Date Issued
February 1, 2022
Journal
Journal of Instrumentation
Publisher
IOP Publishing
Citation
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).
Version
Author's final manuscript
Abstract
Abstract
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.
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.
MIT Department
Massachusetts Institute of Technology. Department of Physics
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1088/1748-0221/17/02/p02022
