Filtering of pulses from particle detectors using neural networks by dimensionality reduction
Identifiers
Permanent link (URI): http://hdl.handle.net/10017/59256DOI: 10.1016/j.nima.2019.162372
ISSN: 0168-9002
Publisher
Elsevier
Date
2019-10-21Funders
Junta de Comunidades de Castilla-La Mancha
Agencia Estatal de Investigación
Bibliographic citation
Regadío Carretero, A., Sánchez Prieto & Esteban, L., S. 2019, “Filtering of pulses from particle detectors using neural networks by dimensionality reduction”, Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, vol. 942, art. no. 162372.
Keywords
Digital pulse processing
Pulse filtering
Noise
Dimensionality reduction
Denoising
Autoencoder
Restricted Boltzmann machine
Neural network
Project
info:eu-repo/grantAgreement/JCCM//PPII10-0150-6529
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/ESP2017-88436-R/ES/ENERGETIC PARTICLE DETECTOR EN SOLAR ORBITER: FASES D Y E/
Document type
info:eu-repo/semantics/article
Version
info:eu-repo/semantics/acceptedVersion
Publisher's version
https://doi.org/10.1016/j.nima.2019.162372Rights
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
© 2019 Elsevier
Access rights
info:eu-repo/semantics/openAccess
Abstract
This article presents a comparison between different filtering methods based on dimensionality reduction for pulses generated on particle detectors. This reduction has been carried out using Neural Networks (NNs). In particular, three topologies have been used: Autoencoders (AEs), Denoising Autoencoders (DAEs) and Restricted Boltzmann Machines (RBMs). A detailed explanation of these methods, a noise reduction analysis, filtering with simulated data and processing of pulses from a neutron detector have been carried out to verify the feasibility of using these NNs as pulse filters.
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Filtering_Regadio_NIMPRA_2019.pdf | 695.0Kb |
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