Robust Pulse Shaping and Noise Suppression in Gamma Spectrometry via Particle Filter

High-resolution gamma spectrometry relies on precise signal processing to improve energy resolution and suppress noise, making it indispensable in fields such as nuclear physics, environmental monitoring, and medical imaging. This study investigates advanced pulse shaping and filtering techniques, leveraging particle filter methods to optimize the processing of gamma radiation signals. The particle filter framework effectively addresses the non-linear and non-Gaussian characteristics of spectrometry signals, delivering real-time signal estimation with notable improvements: a 30% increase in Signal-to-Noise Ratio (SNR) and a 25% reduction in Mean Squared Error (MSE) under low-intensity conditions, and respective gains of 30% and 20% under high-noise conditions. Performance is assessed using MSE and SNR metrics, demonstrating marked enhancements in signal quality. Compared to conventional filtering techniques, the proposed method achieves superior energy resolution and noise mitigation. These results underscore the potential of particle filters as a powerful tool for advancing next-generation high-resolution gamma spectrometry systems, enhancing the precision and reliability of radiation monitoring for nuclear safeguards and medical imaging.