Storage of nuclear waste suitable for non-invasive monitoring using muon scattering tomography

Over the past few decades, research in a form of Non-Destructive Evaluation (NDE), muon tomography, also known as muography, has gained significant attention. In this study, GEANT4 simulations have been used to evaluate the performance of various position-sensitive particle detectors for muography....

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:EPJ Web of conferences Jg. 337; S. 1123
Hauptverfasser: Mukherjee, Indira, Pallav, Piyush, Roy, Promita, Das, Subhendu, Tripathy, Shridhar, Bhattacharya, Purba, Majumdar, Nayana, Mukhopadhyay, Supratik
Format: Journal Article Tagungsbericht
Sprache:Englisch
Veröffentlicht: Les Ulis EDP Sciences 01.01.2025
Schlagworte:
Algorithms
Image reconstruction
Machine learning
Muons
Nondestructive testing
Pattern analysis
Pattern recognition
Performance evaluation
Position sensing
Radiation counters
Radioactive wastes
Tomography
ISSN:2100-014X, 2101-6275, 2100-014X
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Over the past few decades, research in a form of Non-Destructive Evaluation (NDE), muon tomography, also known as muography, has gained significant attention. In this study, GEANT4 simulations have been used to evaluate the performance of various position-sensitive particle detectors for muography. Detectors with position resolutions ranging from 0 µm (ideal one) to 1000 µm were tested, focusing on developing an imaging algorithm to monitor nuclear waste drums. Additionally, the shape of materials inside nuclear waste drums was analyzed using a Pattern Recognition Method (PRM). The study highlights the importance of storage geometry, track reconstruction and machine-learning algorithms.
Bibliographie:ObjectType-Conference Proceeding-1
SourceType-Conference Papers & Proceedings-1
content type line 21
ISSN:2100-014X
2101-6275
2100-014X
DOI:10.1051/epjconf/202533701123