Speaker
Description
Experimental physics often involves detecting weak, time-varying signals embedded in noise-challenges common to both nuclear and biomedical systems. This work explores techniques for extracting and analyzing non-sinusoidal oscillations in noisy, multichannel datasets. Using time-frequency analysis and adaptive denoising methods, we address issues such as amplitude and frequency modulation, phase deviation, and transiet signal detection.
Originally applied to physiological data mimicking dynamic pressure changes in biological systems, these tools are broadly applicable to nuclear epxeriments, including beam diagnostics, gamma-ray detection, and rare eent analysis. The methods enhance signal fidelity and temporal resoltion, offering framework that could improve the acccuracy and interpreability of experimental data in complex, noisy environments.
