TES pulse signal data processing method based on PCA-gradient descent joint algorithm

Firstly, waveforms of all pulses were aligned in time with cross-correlation time delay estimation. Then PCA was used to autonomously extract the primary shape features of the aligned signal pulses. Subsequently, gradient descent was employed on these PCA-derived features to get projections from the pulse data along an optimal vector.

Lastly, the energy represented by each pulse was calibrated based on the known energy points and the spectrum could be obtained in histogram. [Results] Tests on a dataset showed that the energy resolution achieved by our PCA-gradient descent joint algorithm was comparable to that of the traditional OF method. Furthermore, our method demonstrated greater robustness to noise variations.

[Conclusions] The developed method provides an efficient and adaptive alternative for TES signal processing. It exhibits strong potential for application in the high-precision spectroscopy measurements planned with the TES array at SHINE, offering a streamlined workflow without compromising performance.