Spectral baseline estimation using penalized least squares with weights derived from the Bayesian method

The penalized least squares (PLS) method with appropriate weights has proved to be a successful baseline estimation method for various spectral analyses. It can extract the baseline from the spectrum while retaining the signal peaks in the presence of random noise. The algorithm is implemented by iterating over the weights of the data points. In this study, we propose a new approach for assigning weights based on the Bayesian rule. The proposed method provides a self-consistent weighting formula and performs well, particularly for baselines with different curvature components. This method was applied to analyze Schottky spectra obtained in Kr-86 projectile fragmentation measurements in the experimental Cooler Storage Ring (CSRe) at Lanzhou. It provides an accurate and reliable storage lifetime with a smaller error bar than existing PLS methods. It is also a universal baseline-subtraction algorithm that can be used for spectrum-related experiments, such as precision nuclear mass and lifetime measurements in storage rings.

Automated summary

This study proposes a new method for assigning weights based on the Bayesian rule, which performs well in extracting baselines from spectra with different curvatures. The method proves to be more accurate and reliable with a smaller error range in experiments. It can also be applied to various spectrum-related experiments.