The Convolutional Visual Network for Identification and Reconstruction of NOvA Events
- Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States); et al.
In 2016 the NOvA experiment released results for the observation of oscillations in the vμ and ve channels as well as ve cross section measurements using neutrinos from Fermilab’s NuMI beam. These and other measurements in progress rely on the accurate identification and reconstruction of the neutrino flavor and energy recorded by our detectors. This presentation describes the first application of convolutional neural network technology for event identification and reconstruction in particle detectors like NOvA. The Convolutional Visual Network (CVN) Algorithm was developed for identification, categorization, and reconstruction of NOvA events. It increased the selection efficiency of the ve appearance signal by 40% and studies show potential impact to the vμ disappearance analysis.
- Research Organization:
- Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), High Energy Physics (HEP)
- Contributing Organization:
- NOvA Collaboration
- Grant/Contract Number:
- AC02-07CH11359
- OSTI ID:
- 1423233
- Report Number(s):
- FERMILAB-CONF-16-737; 1638573
- Journal Information:
- Journal of Physics. Conference Series, Vol. 898, Issue 7; Conference: 22nd International Conference on Computing in High Energy and Nuclear Physics, San Francisco, CA, 10/10-10/14/2016; ISSN 1742-6588
- Publisher:
- IOP PublishingCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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