Speaker
Description
MeV gamma-ray astronomy holds the key to studying some of the Universe’s most energetic and dynamic phenomena, including kilonovae, supernovae, and gamma-ray bursts. Yet, this region of the spectrum remains underexplored (the so-called “MeV Gap”) due to low photon interaction probability, high background levels, and complex signal responses in detectors.
At DTU Space, we address this challenge using AI-driven signal processing for advanced 3D Cadmium Zinc Telluride (CZT) drift strip detectors. By combining physics-based simulations with machine learning, we generate synthetic pulse shapes used to train neural networks, including convolutional neural networks (CNNs) and multilayer perceptrons (MLPs), to accurately reconstruct key photon interaction properties, such as position and energy.
We demonstrate that these neural networks outperform conventional algorithms, particularly near detector boundaries, enabling more precise event-by-event reconstruction. This work bridges physics-based modeling and modern deep learning to deliver compact, high-resolution detector systems suited for future space missions, astrophysical observations, medical imaging and nuclear safety applications.
This research is part of the i-RASE project (Intelligent Radiation Sensor Readout Systems), funded by Horizon EU, aiming to develop next-generation radiation detection technologies through the integration of AI, advanced sensor design, and application-driven innovation.
| Broad physics domain | High-Energy Astrophysics, Radiation Detection Physics |
|---|---|
| AI/ML technique(s) to be presented | Supervised deep learning using CNNs and MLPs trained on synthetic data from physics-based detector simulations |
