期刊
JOURNAL OF INSTRUMENTATION
卷 11, 期 -, 页码 -出版社
IOP Publishing Ltd
DOI: 10.1088/1748-0221/11/09/P09001
关键词
Particle identification methods; Pattern recognition, cluster finding, calibration and fitting methods; Neutrino detectors; Particle tracking detectors
资金
- US Department of Energy
- US National Science Foundation
- Department of Science and Technology, India
- European Research Council
- MSMT CR, Czech Republic
- RAS, Russia
- RMES, Russia
- RFBR, Russia
- CNPq, Brazil
- FAPEG, Brazil
- State of Minnesota
- US DOE [De-AC02-07CH11359]
- University of Minnesota
- Direct For Mathematical & Physical Scien
- Division Of Physics [1506309] Funding Source: National Science Foundation
- Division Of Physics
- Direct For Mathematical & Physical Scien [0955456, GRANTS:14061722] Funding Source: National Science Foundation
Convolutional neural networks (CNNs) have been widely applied in the computer vision community to solve complex problems in image recognition and analysis. We describe an application of the CNN technology to the problem of identifying particle interactions in sampling calorimeters used commonly in high energy physics and high energy neutrino physics in particular. Following a discussion of the core concepts of CNNs and recent innovations in CNN architectures related to the field of deep learning, we outline a specific application to the NOvA neutrino detector. This algorithm, CVN (Convolutional Visual Network) identifies neutrino interactions based on their topology without the need for detailed reconstruction and outperforms algorithms currently in use by the NOvA collaboration.
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