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Considerations for Training an Artificial Neural Network for Particle Type Identification
- Source :
- IEEE Transactions on Nuclear Science. 68:2350-2357
- Publication Year :
- 2021
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2021.
-
Abstract
- In the nuclear sciences and radiation detection fields, the differentiation between gamma-ray and neutron interactions inside a detector volume continues to be an area of active research. Historically, the primary mechanism for conducting particle identification has been pulse shape discrimination (PSD). However, almost all variations of this technique rely on only two factors: the area of the tail and the total area of the pulse. In the last decade, the emergence of advanced machine learning techniques, most specifically artificial neural networks (ANNs), offers a unique opportunity to capitalize on the entirety of the waveform. But such techniques appear highly reliant on the quality of datasets used for training. Our research addresses this challenge to quantify the relative performances of networks trained on a variety of datasets and subjected to the same test. Furthermore, we offer an analysis of the portability of a network trained on one detector to a similar detector.
- Subjects :
- Nuclear and High Energy Physics
Artificial neural network
010308 nuclear & particles physics
Computer science
business.industry
Detector
Pattern recognition
01 natural sciences
Particle detector
Particle identification
Software portability
Identification (information)
Signal-to-noise ratio
Nuclear Energy and Engineering
0103 physical sciences
Waveform
Artificial intelligence
Electrical and Electronic Engineering
business
Subjects
Details
- ISSN :
- 15581578 and 00189499
- Volume :
- 68
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Nuclear Science
- Accession number :
- edsair.doi...........4ea2c19add93e3ccc0b5282b49b56971