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Title: Revealing Fundamental Physics from the Daya Bay Neutrino Experiment Using Deep Neural Networks

Abstract

Experiments in particle physics produce enormous quantities of data that must be analyzed and interpreted by teams of physicists. This analysis is often exploratory, where scientists are unable to enumerate the possible types of signal prior to performing the experiment. Thus, tools for summarizing, clustering, visualizing and classifying high-dimensional data are essential. Here in this work, we show that meaningful physical content can be revealed by transforming the raw data into a learned high-level representation using deep neural networks, with measurements taken at the Daya Bay Neutrino Experiment as a case study. We further show how convolutional deep neural networks can provide an effective classification filter with greater than 97% accuracy across different classes of physics events, significantly better than other machine learning approaches.

Authors:
 [1];  [2];  [3];  [1];  [1];  [1];  [3];  [1]
  1. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  2. Seoul National Univ. (Korea, Republic of)
  3. Univ. of California, Irvine, CA (United States)
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), High Energy Physics (HEP) (SC-25)
OSTI Identifier:
1379700
Grant/Contract Number:
AC02-05CH11231
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)
Additional Journal Information:
Journal Name: 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA); Conference: 15. IEEE International Conference on Machine Learning and Applications (ICMLA) , Anaheim, CA (United States), 18-20 Dec 2016
Country of Publication:
United States
Language:
English
Subject:
60 APPLIED LIFE SCIENCES; 71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS; High-Energy Physics; Unsupervised Learning; Deep Learning; Autoencoders; Computer architecture; Mesons; Convolution; Data visualization; Detectors; Physics; Neural networks

Citation Formats

Racah, Evan, Ko, Seyoon, Sadowski, Peter, Bhimji, Wahid, Tull, Craig, Oh, Sang-Yun, Baldi, Pierre, and Prabhat, . Revealing Fundamental Physics from the Daya Bay Neutrino Experiment Using Deep Neural Networks. United States: N. p., 2017. Web. doi:10.1109/ICMLA.2016.0160.
Racah, Evan, Ko, Seyoon, Sadowski, Peter, Bhimji, Wahid, Tull, Craig, Oh, Sang-Yun, Baldi, Pierre, & Prabhat, . Revealing Fundamental Physics from the Daya Bay Neutrino Experiment Using Deep Neural Networks. United States. doi:10.1109/ICMLA.2016.0160.
Racah, Evan, Ko, Seyoon, Sadowski, Peter, Bhimji, Wahid, Tull, Craig, Oh, Sang-Yun, Baldi, Pierre, and Prabhat, . Thu . "Revealing Fundamental Physics from the Daya Bay Neutrino Experiment Using Deep Neural Networks". United States. doi:10.1109/ICMLA.2016.0160. https://www.osti.gov/servlets/purl/1379700.
@article{osti_1379700,
title = {Revealing Fundamental Physics from the Daya Bay Neutrino Experiment Using Deep Neural Networks},
author = {Racah, Evan and Ko, Seyoon and Sadowski, Peter and Bhimji, Wahid and Tull, Craig and Oh, Sang-Yun and Baldi, Pierre and Prabhat, .},
abstractNote = {Experiments in particle physics produce enormous quantities of data that must be analyzed and interpreted by teams of physicists. This analysis is often exploratory, where scientists are unable to enumerate the possible types of signal prior to performing the experiment. Thus, tools for summarizing, clustering, visualizing and classifying high-dimensional data are essential. Here in this work, we show that meaningful physical content can be revealed by transforming the raw data into a learned high-level representation using deep neural networks, with measurements taken at the Daya Bay Neutrino Experiment as a case study. We further show how convolutional deep neural networks can provide an effective classification filter with greater than 97% accuracy across different classes of physics events, significantly better than other machine learning approaches.},
doi = {10.1109/ICMLA.2016.0160},
journal = {2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)},
number = ,
volume = ,
place = {United States},
year = {Thu Feb 02 00:00:00 EST 2017},
month = {Thu Feb 02 00:00:00 EST 2017}
}

Journal Article:
Free Publicly Available Full Text
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  • The neutrino mixing angle {theta}13 is currently known to be small but had not been determined. The value of {theta}13 is vital to resolving the neutrino mass heirarchy as well as future investigation of CP violation in the lepton sector. The Daya Bay Reactor Neutrino Experiment is designed to reach a sensitivity of 0.01 or better in sin22{theta}13 via an electron antineutrino disappearance signature.
  • The Daya Bay reactor neutrino experiment is designed to study the disappearance of antineutrinos from the Daya Bay nuclear power plant in China. The goal of this experiment is to measure the remaining unknown neutrino mixing parameter {theta}{sub 13} with high precision: sin{sup 2}(2{theta}{sub 13})<0.01. The experiment is presently under construction and it is anticipated that data acquisition will begin in 2011.
  • Gadolinium loaded liquid scintillator (Gd-LS) is an excellent target material for reactor antineutrino experiments. Ideal Gd-LS should have long attenuation length, high light yield, long term stability, low toxicity, and should be compatible with the material used to build the detector. We have developed a new Gd-LS recipe in which carboxylic acid 3,5,5-trimethylhexanoic acid is used as the complexing ligand to gadolinium, 2,5-diphenyloxazole (PPO) and 1,4-bis[2-methylstyryl]benzene (bis-MSB) are used as primary fluor and wavelength shifter, respectively. The scintillator base is linear alkyl benzene (LAB). Eight hundred liters of Gd-LS has been synthesized and tested in a prototype detector. Results showmore » that the Gd-LS has high quality and is suitable for underground experiments in large quantity. Large scale production facility has been built. A full batch production of 4 t Gd-LS has been produced and monitored for several months. The production of 180 t Gd-LS will be carried out in the near future.« less