| Issue |
EPJ Web Conf.
Volume 302, 2024
Joint International Conference on Supercomputing in Nuclear Applications + Monte Carlo (SNA + MC 2024)
|
|
|---|---|---|
| Article Number | 14002 | |
| Number of page(s) | 10 | |
| Section | Monte Carlo Simulation: Applications / Detectors | |
| DOI | https://doi.org/10.1051/epjconf/202430214002 | |
| Published online | 15 October 2024 | |
https://doi.org/10.1051/epjconf/202430214002
SiC Detector Thickness Optimization for Enhanced Response Variability
1 DES/IRESNE/DTN/SMTA/Nuclear Measurement Laboratory, CEA Cadarache, 13108 Saint-Paul les Durance, France
2 CNRS, IM2NP, Aix-Marseille University, Université de Toulon, 13284, Marseille, France
* Corresponding author: enrica.belfiore@cea.fr
Published online: 15 October 2024
Neutron spectroscopy is a crucial point in several nuclear applications. Accurately measuring fast neutron energy distributions in high-flux conditions reveals a significant technology gap, hindering the acquisition of precise energy fluence distributions. This project investigates the potential of machine learning to bridge this gap, focusing on neutron energies from 100 keV to 20 MeV and fluence rates from 1010 n/cm2s to 1012 n/cm2s using solid detectors such as Silicon Carbide (SiC) and Chemical Vapor Deposition (CVD) diamonds. This paper details the simulation design phase of our project, emphasizing the exploration of optimal SiC solid detector thickness to introduce crucial variability for machine learning training.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.