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Title: Automated Proton Track Identification in MicroBooNE Using Gradient Boosted Decision Trees

Abstract

MicroBooNE is a liquid argon time projection chamber (LArTPC) neutrino experiment that is currently running in the Booster Neutrino Beam at Fermilab. LArTPC technology allows for high-resolution, three-dimensional representations of neutrino interactions. A wide variety of software tools for automated reconstruction and selection of particle tracks in LArTPCs are actively being developed. Short, isolated proton tracks, the signal for low- momentum-transfer neutral current (NC) elastic events, are easily hidden in a large cosmic background. Detecting these low-energy tracks will allow us to probe interesting regions of the proton's spin structure. An effective method for selecting NC elastic events is to combine a highly efficient track reconstruction algorithm to find all candidate tracks with highly accurate particle identification using a machine learning algorithm. We present our work on particle track classification using gradient tree boosting software (XGBoost) and the performance on simulated neutrino data.

Authors:
ORCiD logo [1]
  1. New Mexico State U.
Publication Date:
Research Org.:
Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States)
Sponsoring Org.:
USDOE Office of Science (SC), High Energy Physics (HEP) (SC-25)
Contributing Org.:
MicroBooNE
OSTI Identifier:
1423247
Report Number(s):
arXiv:1710.00898; FERMILAB-CONF-17-440-E
1628407
DOE Contract Number:  
AC02-07CH11359
Resource Type:
Conference
Resource Relation:
Conference: Meeting of the APS Division of Particles and Fields, Batavia, Illinois, USA, 07/31-08/04/2017
Country of Publication:
United States
Language:
English
Subject:
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND TECHNOLOGY; 72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS

Citation Formats

Woodruff, Katherine. Automated Proton Track Identification in MicroBooNE Using Gradient Boosted Decision Trees. United States: N. p., 2017. Web.
Woodruff, Katherine. Automated Proton Track Identification in MicroBooNE Using Gradient Boosted Decision Trees. United States.
Woodruff, Katherine. Mon . "Automated Proton Track Identification in MicroBooNE Using Gradient Boosted Decision Trees". United States. doi:. https://www.osti.gov/servlets/purl/1423247.
@article{osti_1423247,
title = {Automated Proton Track Identification in MicroBooNE Using Gradient Boosted Decision Trees},
author = {Woodruff, Katherine},
abstractNote = {MicroBooNE is a liquid argon time projection chamber (LArTPC) neutrino experiment that is currently running in the Booster Neutrino Beam at Fermilab. LArTPC technology allows for high-resolution, three-dimensional representations of neutrino interactions. A wide variety of software tools for automated reconstruction and selection of particle tracks in LArTPCs are actively being developed. Short, isolated proton tracks, the signal for low- momentum-transfer neutral current (NC) elastic events, are easily hidden in a large cosmic background. Detecting these low-energy tracks will allow us to probe interesting regions of the proton's spin structure. An effective method for selecting NC elastic events is to combine a highly efficient track reconstruction algorithm to find all candidate tracks with highly accurate particle identification using a machine learning algorithm. We present our work on particle track classification using gradient tree boosting software (XGBoost) and the performance on simulated neutrino data.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Mon Oct 02 00:00:00 EDT 2017},
month = {Mon Oct 02 00:00:00 EDT 2017}
}

Conference:
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