Published July 21, 2007
| Version v1
Journal article
Cascade training technique for particle identification
Creators
- 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.