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Spectral baseline estimation using penalized least squares with weights derived from the Bayesian method 被引量:1
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作者 Qian Wang Xin-Liang Yan +3 位作者 Xiang-Cheng Chen Peng Shuai Meng Wang Yu-Hu Zhang 《Nuclear Science and Techniques》 SCIE EI CAS CSCD 2022年第11期144-157,共14页
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 ... 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. 展开更多
关键词 Penalized least squares baseline correction Bayesian rule Spectrum analysis
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