MicroBooNE Investigation of Low-Energy Excess Using Deep Learning Algorithms
Abstract
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, which is potential evidence for new non-Standard Model physics such as sterile neutrinos. I discuss the status of a search for low-energy electron-neutrino interactions within the MicroBooNE detector. This analysis features a hybrid approach of traditional reconstruction methods along with the use of convolutional neural networks (CNNs), a type of deep learning algorithm highly adept at pattern recognition. I describe the identification of events and the ways in which the CNNs are used. I also outline the ways that we are addressing issues related to applying CNNs, which are trained on simulated data, to data from the detector.
Keywords
Cite
@article{arxiv.1710.00468,
title = {MicroBooNE Investigation of Low-Energy Excess Using Deep Learning Algorithms},
author = {Lauren E. Yates},
journal= {arXiv preprint arXiv:1710.00468},
year = {2017}
}
Comments
Talk presented at the APS Division of Particles and Fields Meeting (DPF 2017), July 31-August 4, 2017, Fermilab. C170731