DeepNSI: Element identification in experimental photoneutron spectra for illicit material detection.
Saved in:
| Title: | DeepNSI: Element identification in experimental photoneutron spectra for illicit material detection. |
|---|---|
| Authors: | Besnard-Vauterin, C.1 (AUTHOR) clement.besnardvauterin@cea.fr, Blideanu, V.1 (AUTHOR) valentin.blideanu@cea.fr, Rapp, B.2 (AUTHOR) benjamin.rapp@cea.fr |
| Source: | Applied Radiation & Isotopes. Nov2025, Vol. 225, pN.PAG-N.PAG. 1p. |
| Subjects: | Convolutional neural networks, Spectrum analysis, Deep learning, Nuclear activation analysis, Least squares |
| Abstract: | We present DeepNSI (Deep Neutron Spectrum Identification), a deep learning framework for identifying elemental composition from photon-induced neutron spectra in realistic inspection scenarios. Targeted toward the detection of illicit materials, DeepNSI consists of an ensemble of element-specific convolutional neural networks trained on a hybrid dataset of simulated and experimental photoneutron spectra. Special emphasis is placed on detecting light elements such as nitrogen and oxygen, which are key signatures of explosives and chemical threats. The model incorporates Monte Carlo Dropout to provide predictive uncertainty and employs a post-processing step based on non-negative least squares (NNLS) to reconstruct the experimental spectrum from reference components. Evaluation on real data—including organic compounds and complex configurations involving shielding materials—demonstrates robust element identification, with uncertainty estimates supporting decision confidence. Although NNLS coefficients are influenced by nuclear cross sections and cannot be interpreted as direct concentrations, their trends across samples reflect meaningful compositional differences. These results establish DeepNSI as a reliable tool for interpretable, machine-learning-based elemental analysis in photon interrogation systems coupled with photoneutron spectrometry. • DeepNSI identifies elements in photoneutron spectra using a bank of element-specific convolutional neural networks. • The model is trained on both simulated and experimental spectra, with data augmentation to improve generalization. • Monte Carlo Dropout quantifies uncertainty, supporting confident element detection in cluttered or shielded scenarios. • NNLS post-processing checks prediction consistency by reconstructing the measured spectrum from reference components. • DeepNSI detects nitrogen and oxygen in realistic test cases, including shielded and mixed-material inspection scenarios. [ABSTRACT FROM AUTHOR] |
| Copyright of Applied Radiation & Isotopes is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
|---|---|
| Header | DbId: egs DbLabel: Engineering Source An: 187530501 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: DeepNSI: Element identification in experimental photoneutron spectra for illicit material detection. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Besnard-Vauterin%2C+C%2E%22">Besnard-Vauterin, C.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> clement.besnardvauterin@cea.fr</i><br /><searchLink fieldCode="AR" term="%22Blideanu%2C+V%2E%22">Blideanu, V.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> valentin.blideanu@cea.fr</i><br /><searchLink fieldCode="AR" term="%22Rapp%2C+B%2E%22">Rapp, B.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> benjamin.rapp@cea.fr</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Applied+Radiation+%26+Isotopes%22">Applied Radiation & Isotopes</searchLink>. Nov2025, Vol. 225, pN.PAG-N.PAG. 1p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Convolutional+neural+networks%22">Convolutional neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Spectrum+analysis%22">Spectrum analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Nuclear+activation+analysis%22">Nuclear activation analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Least+squares%22">Least squares</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: We present DeepNSI (Deep Neutron Spectrum Identification), a deep learning framework for identifying elemental composition from photon-induced neutron spectra in realistic inspection scenarios. Targeted toward the detection of illicit materials, DeepNSI consists of an ensemble of element-specific convolutional neural networks trained on a hybrid dataset of simulated and experimental photoneutron spectra. Special emphasis is placed on detecting light elements such as nitrogen and oxygen, which are key signatures of explosives and chemical threats. The model incorporates Monte Carlo Dropout to provide predictive uncertainty and employs a post-processing step based on non-negative least squares (NNLS) to reconstruct the experimental spectrum from reference components. Evaluation on real data—including organic compounds and complex configurations involving shielding materials—demonstrates robust element identification, with uncertainty estimates supporting decision confidence. Although NNLS coefficients are influenced by nuclear cross sections and cannot be interpreted as direct concentrations, their trends across samples reflect meaningful compositional differences. These results establish DeepNSI as a reliable tool for interpretable, machine-learning-based elemental analysis in photon interrogation systems coupled with photoneutron spectrometry. • DeepNSI identifies elements in photoneutron spectra using a bank of element-specific convolutional neural networks. • The model is trained on both simulated and experimental spectra, with data augmentation to improve generalization. • Monte Carlo Dropout quantifies uncertainty, supporting confident element detection in cluttered or shielded scenarios. • NNLS post-processing checks prediction consistency by reconstructing the measured spectrum from reference components. • DeepNSI detects nitrogen and oxygen in realistic test cases, including shielded and mixed-material inspection scenarios. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Applied Radiation & Isotopes is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=187530501 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.apradiso.2025.112014 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 1 StartPage: N.PAG Subjects: – SubjectFull: Convolutional neural networks Type: general – SubjectFull: Spectrum analysis Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Nuclear activation analysis Type: general – SubjectFull: Least squares Type: general Titles: – TitleFull: DeepNSI: Element identification in experimental photoneutron spectra for illicit material detection. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Besnard-Vauterin, C. – PersonEntity: Name: NameFull: Blideanu, V. – PersonEntity: Name: NameFull: Rapp, B. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 11 Text: Nov2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 09698043 Numbering: – Type: volume Value: 225 Titles: – TitleFull: Applied Radiation & Isotopes Type: main |
| ResultId | 1 |
