Progress Toward a Deep-Learning-Based Search for Low-Energy Electron Neutrinos in MicroBooNE
- MIT
MicroBooNE is a neutrino experiment based at Fermilab which consists of a liquid argon time-projection chamber in the Booster Neutrino Beam (BNB). The experiment aims to investigate the excess of electron neutrino-like events seen by the MiniBooNE experiment, also located in the BNB. I will describe the status of our deep-learning-based search for low-energy electron neutrino interactions within the MicroBooNE detector and the muon neutrino interactions by which we will constrain them. This analysis features a novel hybrid approach of traditional reconstruction methods combined with the use of convolutional neural networks, a type of deep learning algorithm highly adept at pattern recognition. I will present work on ongoing studies that will characterize and quantify the systematic uncertainties associated with this analysis.
- Research Organization:
- Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), High Energy Physics (HEP)
- Contributing Organization:
- MicroBooNE
- DOE Contract Number:
- AC02-07CH11359
- OSTI ID:
- 1462053
- Report Number(s):
- FERMILAB-POSTER-18-019-ND; oai:inspirehep.net:1683908
- Country of Publication:
- United States
- Language:
- English
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