Optimization of dead time correction for digital gamma ray spectroscopy based on social spider algorithm

Gamma spectroscopy is a pivotal technique in radiation measurement and monitoring, with applications spanning nuclear physics, environmental science, and medical diagnostics. However, a major challenge in gamma spectroscopy is the dead time effect, which occurs when the detector is unable to register subsequent events while processing previous signals. This phenomenon leads to underestimation of true count rates and compromises the accuracy of spectral analysis. To overcome this limitation, we propose an efficient algorithm based on the Social Spider Optimization (SSO) technique to optimize dead time corrections and enhance the precision of count rate estimation. The SSO algorithm, inspired by the collective foraging behavior of social spiders, is employed to simultaneously optimize the Non-Paralyzable and paralyzable dead times, enabling accurate correction of observed count rates. By considering the complex interaction between multiple parameters, the algorithm provides a more precise correction compared to traditional methods. The performance of the proposed SSO-based algorithm is validated through experimental analysis and a direct comparison with literature-based results, demonstrating its superior accuracy and robustness. The experimental validation, conducted using a High-Purity Germanium (HPGe) detector, revealed significant improvements in the accuracy of count rate corrections. Specifically, the observed count rate, initially recorded at 10,007 counts per second, was corrected to 11,007.71 counts per second with an estimated dead time of 9.08 μs. This corrected count rate closely aligns with the true count rate, showing excellent agreement with literature-reported values. • First use of Social Spider Optimization to optimize paralyzable and non-paralyzable dead times. • Validated on HPGe detector with 11007.71 counts/sec and 9.08 μs dead time, close to true counts. • Outperforms conventional methods, ensuring robust precision in nuclear, medical, and monitoring fields. • Bridges nuclear instrumentation, AI, and swarm optimization for advanced analytical applications.