Advances in X-ray and Gamma RAy DEtectors and applications (XGRADE 2026)

Session

Data Analysis and Machine Learning 1

26 Mar 2026, 14:30

Aula Capitò ( Università degli Studi di Palermo)

Conveners

Data Analysis and Machine Learning 1

• Maurizio Marrale (University of Palermo)

16. Machine Learning background suppression for CZT detectors using Monte Carlo simulations

Simone Manti

26/03/2026, 14:30

Cadmium Zinc Telluride (CZT) detectors are a powerful platform for radiation detection in the keV energy range, combining room-temperature operation and a compact geometry. Their deployment in accelerator environments opens new experimental opportunities, but also introduces complex and structured background conditions that might limit their sensitivities. In this work, we present a Machine...

25. CadTool - a end-to-end x-ray/gamma-ray solid state detector simulation framework

Mr José Sousa (Université Paris-Saclay, CEA IRFU, AIM)

26/03/2026, 15:00

CadTool is an open source simulation framework for x-ray and gamma-ray solid state detectors. The tool uses the SolidStateDetectors.jl library for the simulation of detector electric and weighting fields and implements a modified charge drift model to take into consideration initial charge cloud size, self-repulsion, charge trapping and thermal diffusion. The estimation of the initial charge...

11. Machine Learning–Driven Design of High-Performance X-ray and Gamma-Ray Detectors

Prof. Murat Aydemir (Erzurum Technical University)

26/03/2026, 15:30

The development of high-performance X-ray and gamma-ray detectors is fundamentally constrained by the need to balance detection efficiency, signal stability, material thickness, and long-term radiation tolerance.[1–3] Despite extensive experimental efforts, the existing literature remains fragmented across material classes and device architectures, limiting the establishment of generalizable...