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Neutron-gamma discrimination based on STFT-DFF model and FPGA implementation.

Bingqi Liu1, Yufeng Tang2, Xianghe Liu3

  • 1College of Nuclear Technology and Automation Engineering, Chengdu University of Technology, Chengdu, 610059, Sichuan, China; College of Electronic Information and Electrical Engineering, Chengdu University, Chengdu, 610106, Sichuan, China.

Applied Radiation and Isotopes : Including Data, Instrumentation and Methods for Use in Agriculture, Industry and Medicine
|March 21, 2026
PubMed
Summary

This study introduces STFT-DFF, a novel machine learning algorithm for neutron-gamma discrimination. It achieves superior performance and real-time processing on FPGA, overcoming limitations of traditional methods.

Keywords:
Dynamic feature fusion (DFF)FPGA hardware deploymentNeutron-gamma discriminationShort-time Fourier transform (STFT)

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Area of Science:

  • Nuclear Physics
  • Signal Processing
  • Machine Learning

Background:

  • Traditional neutron-gamma discrimination methods struggle with low-energy detection.
  • Existing machine learning algorithms face challenges in hardware deployment and real-time application.

Purpose of the Study:

  • To develop a real-time neutron-gamma discrimination algorithm suitable for FPGA deployment.
  • To improve discrimination performance, especially in the low-energy region, and enhance real-time processing efficiency.

Main Methods:

  • Implemented a novel machine learning algorithm, STFT-DFF, integrating Short-Time Fourier Transform (STFT) and Dynamic Feature Fusion (DFF).
  • Utilized STFT to convert pulse signals into time-frequency feature vectors, extracting neutron and gamma-ray differences.
  • Employed a lightweight DFF model with an attention mechanism for adaptive feature enhancement and noise suppression.

Main Results:

  • The STFT-DFF algorithm demonstrated significantly improved Figure of Merit (FOM) compared to the Charge Comparison Method (CCM) on two distinct datasets.
  • Achieved real-time pulse discrimination processing efficiency exceeding 20,000 pulses per second after FPGA deployment.
  • Successfully extracted time-frequency feature differences, enhancing discrimination accuracy.

Conclusions:

  • The proposed STFT-DFF algorithm offers a robust solution for real-time neutron-gamma discrimination.
  • FPGA deployment enables high-speed processing, making the algorithm practical for various applications.
  • The integration of STFT and DFF effectively addresses the limitations of previous methods.