Neural Network-Based Signal Processing for Enhanced Radiation Detection

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Abstract

Integrating Artificial Intelligence (AI) and Machine Learning (ML) into signal processing can enhance high-resolution radiation detectors such as the 3D CZT drift strip detector at DTU Space, designed for space and medical applications. This study investigates Neural Network (NN) models for predicting radiation interaction positions, trained on synthetic data and evaluated with experimental data. A Feed-Forward Neural Network (FFNN) achieved comparable or improved positioning accuracy over conventional algorithms, notably near detector boundaries, aided by randomized synthetic electronic noise. Despite model and data limitations, the NN approach shows strong potential for AI-driven signal processing in space, healthcare, and security applications.
Original languageEnglish
Title of host publication2025 IEEE Nuclear Science Symposium (NSS), Medical Imaging Conference (MIC) and Room Temperature Semiconductor Detector Conference (RTSD)
Number of pages2
PublisherIEEE
Publication date2025
ISBN (Electronic)978-1-6654-7767-3
DOIs
Publication statusPublished - 2025
Event2025 IEEE Nuclear Science Symposium (NSS), Medical Imaging Conference (MIC) and Room Temperature Semiconductor Detector Conference (RTSD) - Yokohama, Japan
Duration: 1 Nov 20258 Nov 2025

Conference

Conference2025 IEEE Nuclear Science Symposium (NSS), Medical Imaging Conference (MIC) and Room Temperature Semiconductor Detector Conference (RTSD)
Country/TerritoryJapan
CityYokohama
Period01/11/202508/11/2025
SeriesNuclear Science Symposium & Medical Imaging Conference
ISSN2577-0829

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