Gamma-ray spectroscopy with deep learning

Signal processing and deep learning for gamma-ray and X-ray spectrometry

Compensation of hardware limitations in gamma-ray spectrometry using modern signal processing and deep learning, in long-standing collaboration with Dr. Tom Trigano (SCE) and Prof. Yongxin Zhu’s group (Shanghai Advanced Research Institute, CAS).

Topics:

  • Activity estimation from short-duration recordings
  • Pile-up correction at high count rates
  • Energy spectrum estimation
  • Time-domain simulators for algorithm development
  • Self-supervised and contrastive learning for spectroscopic pulses
  • Open datasets for benchmarking

Representative publications:

  • T. Trigano and D. Bykhovsky, “Deep Learning Based Method for Activity Estimation from Short-Duration Gamma Spectroscopy Recordings,” IEEE Transactions on Instrumentation and Measurement, vol. 73, pp. 1–11, 2024. doi:10.1109/TIM.2024.3359146
  • Z. Chen, D. Bykhovsky, X. Zheng, T. Trigano, Y. Zhu, “GaSim: A Python Class to Generate Simulated Time Signals for Gamma Spectroscopy,” SoftwareX, vol. 29, p. 102037, Feb. 2025. doi:10.1016/j.softx.2024.102037
  • D. Bykhovsky, Z. Chen, Y. Huang, X. Zheng, T. Trigano, “Advanced Spectroscopy Time-Domain Signal Simulator for the Development of Machine and Deep Learning Algorithms,” IEEE Sensors Letters, vol. 9, no. 2, pp. 1–4, Feb. 2025. doi:10.1109/LSENS.2025.3524623
  • Y. Huang, X. Zheng, Y. Zhu, T. Trigano, D. Bykhovsky, Z. Chen, “Deep learning pile-up correction algorithm for spectrometric data under high count rate measurements,” Sensors, vol. 25, no. 5, p. 1464, Feb. 2025. doi:10.3390/s25051464
  • Y. Huang, D. Bykhovsky, T. Trigano, Z. Chen, X. Zheng, Y. Zhu, “Deep Learning Based Energy Spectrum Estimation for High Counting Rate Gamma-Ray Spectrometry,” IEEE Transactions on Instrumentation and Measurement, vol. 74, pp. 1–14, 2025. doi:10.1109/TIM.2025.3526154
  • C. Lin, X. Zheng, T. Trigano, D. Bykhovsky, Y. Zhu, L. Tian, “Spectroscopic Pulse Embeddings by Contrastive Learning from Unlabeled Data for Pile-Up Analysis,” Sensors, 2026. doi:10.3390/s26072138
  • C. Lin, Z. Chen, C. Feng, S. Gu, X. Zheng, Y. Zhu, T. Trigano, D. Bykhovsky, “An open X-ray spectrometric dataset for deep learning-based pile-up correction,” WASA, Tokyo, Japan, Jun. 2025.