Reconstruction of Compton Edges in Plastic Gamma Spectra Using Deep Autoencoder

Cited 7 time in webofscience Cited 6 time in scopus
  • Hit : 396
  • Download : 159
DC FieldValueLanguage
dc.contributor.authorJeon, Byoungilko
dc.contributor.authorLee, Youhanko
dc.contributor.authorMoon, Myungkookko
dc.contributor.authorKim, Jongyulko
dc.contributor.authorCho, Gyuseongko
dc.date.accessioned2020-07-03T03:20:10Z-
dc.date.available2020-07-03T03:20:10Z-
dc.date.created2020-06-29-
dc.date.created2020-06-29-
dc.date.issued2020-05-
dc.identifier.citationSENSORS, v.20, no.10-
dc.identifier.issn1424-8220-
dc.identifier.urihttp://hdl.handle.net/10203/275184-
dc.description.abstractPlastic scintillation detectors are widely utilized in radiation measurement because of their unique characteristics. However, they are generally used for counting applications because of the energy broadening effect and the absence of a photo peak in their spectra. To overcome their weaknesses, many studies on pseudo spectroscopy have been reported, but most of them have not been able to directly identify the energy of incident gamma rays. In this paper, we propose a method to reconstruct Compton edges in plastic gamma spectra using an artificial neural network for direct pseudo gamma spectroscopy. Spectra simulated using MCNP 6.2 software were used to generate training and validation sets. Our model was trained to reconstruct Compton edges in plastic gamma spectra. In addition, we aimed for our model to be capable of reconstructing Compton edges even for spectra having poor counting statistics by designing a dataset generation procedure. Minimum reconstructible counts for single isotopes were evaluated with metric of mean averaged percentage error as 650 for Co-60, 2000 for Cs-137, 3050 for Na-22, and 3750 for Ba-133. The performance of our model was verified using the simulated spectra measured by a PVT detector. Although our model was trained using simulation data only, it successfully reconstructed Compton edges even in measured gamma spectra with poor counting statistics.-
dc.languageEnglish-
dc.publisherMDPI-
dc.titleReconstruction of Compton Edges in Plastic Gamma Spectra Using Deep Autoencoder-
dc.typeArticle-
dc.identifier.wosid000539323700149-
dc.identifier.scopusid2-s2.0-85085255017-
dc.type.rimsART-
dc.citation.volume20-
dc.citation.issue10-
dc.citation.publicationnameSENSORS-
dc.identifier.doi10.3390/s20102895-
dc.contributor.localauthorCho, Gyuseong-
dc.contributor.nonIdAuthorLee, Youhan-
dc.contributor.nonIdAuthorMoon, Myungkook-
dc.contributor.nonIdAuthorKim, Jongyul-
dc.description.isOpenAccessY-
dc.type.journalArticleArticle-
dc.subject.keywordAuthorplastic gamma spectra-
dc.subject.keywordAuthorenergy broadening correction-
dc.subject.keywordAuthorCompton edge reconstruction-
dc.subject.keywordAuthordeep learning-
dc.subject.keywordAuthordeep autoencoder-
dc.subject.keywordPlusENERGY-WEIGHTED ALGORITHM-
dc.subject.keywordPlusIDENTIFICATION ALGORITHM-
dc.subject.keywordPlusNETWORK-
dc.subject.keywordPlusPVT-
This item is cited by other documents in WoS
⊙ Detail Information in WoSⓡ Click to see webofscience_button
⊙ Cited 7 items in WoS Click to see citing articles in records_button

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0