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Title: Decision Tree Technique for Particle Identification

Abstract

Particle identification based on measurements such as the Cerenkov angle, momentum, and the rate of energy loss per unit distance (-dE/dx) is fundamental to the BaBar detector for particle physics experiments. It is particularly important to separate the charged forms of kaons and pions. Currently, the Neural Net, an algorithm based on mapping input variables to an output variable using hidden variables as intermediaries, is one of the primary tools used for identification. In this study, a decision tree classification technique implemented in the computer program, CART, was investigated and compared to the Neural Net over the range of momenta, 0.25 GeV/c to 5.0 GeV/c. For a given subinterval of momentum, three decision trees were made using different sets of input variables. The sensitivity and specificity were calculated for varying kaon acceptance thresholds. This data was used to plot Receiver Operating Characteristic curves (ROC curves) to compare the performance of the classification methods. Also, input variables used in constructing the decision trees were analyzed. It was found that the Neural Net was a significant contributor to decision trees using dE/dx and the Cerenkov angle as inputs. Furthermore, the Neural Net had poorer performance than the decision tree technique, but tendedmore » to improve decision tree performance when used as an input variable. These results suggest that the decision tree technique using Neural Net input may possibly increase accuracy of particle identification in BaBar.« less

Authors:
Publication Date:
Research Org.:
SLAC National Accelerator Lab., Menlo Park, CA (United States)
Sponsoring Org.:
USDOE Office of Science (US)
OSTI Identifier:
815649
Report Number(s):
SLAC-TN-03-019
TRN: US0304757
DOE Contract Number:  
AC03-76SF00515
Resource Type:
Technical Report
Resource Relation:
Other Information: PBD: 5 Sep 2003
Country of Publication:
United States
Language:
English
Subject:
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS; ACCURACY; ALGORITHMS; CLASSIFICATION; COMPUTER CODES; HIDDEN VARIABLES; KAONS; PARTICLE IDENTIFICATION; PERFORMANCE; PHYSICS; PIONS; SENSITIVITY; SPECIFICITY; DECISION TREE ANALYSIS

Citation Formats

Quiller, Ryan. Decision Tree Technique for Particle Identification. United States: N. p., 2003. Web. doi:10.2172/815649.
Quiller, Ryan. Decision Tree Technique for Particle Identification. United States. https://doi.org/10.2172/815649
Quiller, Ryan. 2003. "Decision Tree Technique for Particle Identification". United States. https://doi.org/10.2172/815649. https://www.osti.gov/servlets/purl/815649.
@article{osti_815649,
title = {Decision Tree Technique for Particle Identification},
author = {Quiller, Ryan},
abstractNote = {Particle identification based on measurements such as the Cerenkov angle, momentum, and the rate of energy loss per unit distance (-dE/dx) is fundamental to the BaBar detector for particle physics experiments. It is particularly important to separate the charged forms of kaons and pions. Currently, the Neural Net, an algorithm based on mapping input variables to an output variable using hidden variables as intermediaries, is one of the primary tools used for identification. In this study, a decision tree classification technique implemented in the computer program, CART, was investigated and compared to the Neural Net over the range of momenta, 0.25 GeV/c to 5.0 GeV/c. For a given subinterval of momentum, three decision trees were made using different sets of input variables. The sensitivity and specificity were calculated for varying kaon acceptance thresholds. This data was used to plot Receiver Operating Characteristic curves (ROC curves) to compare the performance of the classification methods. Also, input variables used in constructing the decision trees were analyzed. It was found that the Neural Net was a significant contributor to decision trees using dE/dx and the Cerenkov angle as inputs. Furthermore, the Neural Net had poorer performance than the decision tree technique, but tended to improve decision tree performance when used as an input variable. These results suggest that the decision tree technique using Neural Net input may possibly increase accuracy of particle identification in BaBar.},
doi = {10.2172/815649},
url = {https://www.osti.gov/biblio/815649}, journal = {},
number = ,
volume = ,
place = {United States},
year = {Fri Sep 05 00:00:00 EDT 2003},
month = {Fri Sep 05 00:00:00 EDT 2003}
}