Published July 21, 2007 | Version v1
Journal article

Cascade training technique for particle identification

  • 1. Department of Physics and Astronomy, University of Alabama, Tuscaloosa, AL 35487 (United States)

Description

The cascade training technique which was developed during our work on the MiniBooNE particle identification has been found to be a very efficient way to improve the selection performance, especially when very low background contamination levels are desired. The detailed description of this technique is presented here based on the MiniBooNE detector Monte Carlo simulations, using both artificial neural networks and boosted decision trees as examples

Additional details

Identifiers

DOI
10.1016/j.nima.2007.05.173;
arXiv
arXiv:physics/0611267v1;
PII
S0168-9002(07)00840-6;

Publishing Information

Journal Title
Nuclear Instruments and Methods in Physics Research. Section A, Accelerators, Spectrometers, Detectors and Associated Equipment
Journal Volume
578
Journal Issue
1
Journal Page Range
p. 315-321
ISSN
0168-9002
CODEN
NIMAER

INIS

Country of Publication
Netherlands
Country of Input or Organization
International Atomic Energy Agency (IAEA)
INIS RN
39010424
Subject category
S46: INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND TECHNOLOGY;
Descriptors DEI
COMPUTERIZED SIMULATION; COMPUTERS; DATA ANALYSIS; DECISION TREE ANALYSIS; MONTE CARLO METHOD; NEURAL NETWORKS; NEUTRINO OSCILLATION; PARTICLE IDENTIFICATION; PERFORMANCE
Descriptors DEC
CALCULATION METHODS; SIMULATION

Optional Information

Copyright
Copyright (c) 2007 Elsevier Science B.V., Amsterdam, The Netherlands, All rights reserved.