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

he penalized least squares (PLS) method with appropriate weights has proven to be a successful baselineestimation method for various spectral analyses. It can extract the baseline from the spectrum while retainingthe signal peaks in the presence of random noise. The algorithm is implemented by iterating over the weightsof the data points. In this study, we propose a new approach for assigning weights based on the Bayesianrule. The proposed method provides a self-consistent weighting formula and performs well, particularly forbaselines with different curvature components. This method was applied to analyze Schottky spectra obtainedin86Kr projectile fragmentation measurements in the experimental Cooler Storage Ring (CSRe) at Lanzhou. Itprovides an accurate and reliable storage lifetime with a smaller error bar than existing PLS methods. It is alsoa universal baseline-subtraction algorithm that can be used for spectrum-related experiments, such as precisionnuclear mass and lifetime measurements in storage rings.