Deep Learning Based Energy Spectrum Estimation for High Counting Rate Nuclear Spectrometry
Accepted manuscript. IEEE Transactions on Instrumentation and Measurement, vol. 74, pp. 1-14, 2025.
Y. Huang, C. Lin, D. Bykhovsky, T. Trigano, Z. Chen, X. Zheng, Y. Zhu, “Deep Learning Based Energy Spectrum Estimation for High Counting Rate Nuclear Spectrometry,” IEEE Transactions on Instrumentation and Measurement, vol. 74, pp. 1–14, 2025.
- Published version: doi:10.1109/TIM.2025.3573370
- Accepted manuscript (PDF)
- Project: Gamma-ray spectroscopy with deep learning
The accepted manuscript linked above is self-archived under the publisher’s green open-access policy. © IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.