Hard X-Ray Emission Detection Using Deep Learning Analysis of the Radiated UHF Electromagnetic Signal from a Plasma Focus Discharge

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dc.contributor.authorAvaria, Gonzalo
dc.contributor.authorArdila-Rey, Jorge
dc.contributor.authorDavis, Sergio
dc.contributor.authorOrellana, Luis
dc.contributor.authorCevallos, Benjamin
dc.contributor.authorPavez, Cristian
dc.contributor.authorSoto, Leopoldo
dc.date.accessioned2022-11-22T15:05:35Z
dc.date.available2022-11-22T15:05:35Z
dc.date.issued2019
dc.descriptionIndexación: Scopus
dc.description.abstractA method to determine the presence of hard X-ray emission processes from a dense plasma focus (205 J, 22 kV, 6.5 mbar H2) using Ultra High Frequency (UHF) measurements and deep learning techniques is presented. Simultaneously, the electromagnetic UHF radiation emitted from the plasma focus was measured with a Vivaldi UHF antenna, while the hard X-ray emission was measured with a scintillator-photomultiplier system. A classification algorithm based on deep learning methods, using two-dimensional convolutional layers, was implemented to predict the hard X-ray signal standard deviation value using only the antenna signal measurement. Two independent datasets, consisting of 999 and 1761 data pairs each, were used in the analysis. Different realizations of the training/validation process using a deep learning model, obtained overall better results in comparison to other machine learning methods like k-neighbors, decision trees, gradient boost, and random forest. The results of the deep learning algorithm, and even its comparison with other machine learning methods, indicate that a relationship between the electromagnetic UHF radiation and hard X-ray emission can be established, enabling the indirect detection of hard X-ray pulses only using the UHF antenna signal. This indirect detection presents the opportunity to have a simple and low-cost diagnostic, compared to the methods currently used to characterize the pulses of X-rays emitted from plasma focus discharges. © 2013 IEEE.es
dc.description.urihttps://ieeexplore-ieee-org.recursosbiblioteca.unab.cl/stamp/stamp.jsp?tp=&arnumber=8731859
dc.identifier.doi10.1109/ACCESS.2019.2921288
dc.identifier.issn2169-3536
dc.identifier.urihttps://repositorio.unab.cl/xmlui/handle/ria/24866
dc.language.isoenes
dc.publisherInstitute of Electrical and Electronics Engineers Inc.es
dc.rights.licenseAttribution-ShareAlike 4.0 International
dc.rights.urihttps://v2.sherpa.ac.uk/id/publication/24685
dc.subjectDeep learninges
dc.subjectPlasma focuses
dc.subjectUHF antennaes
dc.subjectX-ray pulsees
dc.titleHard X-Ray Emission Detection Using Deep Learning Analysis of the Radiated UHF Electromagnetic Signal from a Plasma Focus Dischargees
dc.typeArtículoes
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IEEE Access Volume 7, Pages 74899 - 749082019 Article number 8731859
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