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

作　　者：Qian Wang Xin-Liang Yan Xiang-Cheng Chen Peng Shuai Meng Wang Yu-Hu Zhang 

机构地区：[1]Institute of Modern Physics,Chinese Academy of Sciences,Lanzhou 730000,China [2]University of Chinese Academy of Sciences,Beijing 100049,China [3]Van Swinderen Institute,University of Groningen,Groningen 9747 AA,The Netherlands

出　　处：《Nuclear Science and Techniques》2022年第11期144-157,共14页核技术（英文）

基　　金：supported by the National Key R&D Program of China(No.2018YFA0404401);CAS Project for Young Scientists in Basic Research(No.YSBR-002);Strategic Priority Research Program of the Chinese Academy of Sciences(No.XDB34000000).

摘　　要：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 86Kr 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.

关 键 词：Penalized least squares Baseline correction Bayesian rule Spectrum analysis 

分 类 号：O433[机械工程—光学工程]