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

Qian Wang; Xin-Liang Yan; Xiang-Cheng Chen; Peng Shuai; Meng Wang; Yu-Hu Zhang

doi:10.1007/s41365-022-01132-9

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

NUCLEAR PHYSICS AND INTERDISCIPLINARY RESEARCH | Updated：2022-12-16

- Spectral baseline estimation using penalized least squares with weights derived from the Bayesian method
- Nuclear Science and Techniques Vol. 33, Issue 11, Article number: 148(2022)
- Affiliations：
  
  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
- Author bio：
  
  † wangqian2016@impcas.ac.cn
- Funds：
- DOI：10.1007/s41365-022-01132-9
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Qian Wang, Xin-Liang Yan, Xiang-Cheng Chen, et al. Spectral baseline estimation using penalized least squares with weights derived from the Bayesian method. [J]. Nuclear Science and Techniques 33(11):148(2022)
DOI：

Qian Wang, Xin-Liang Yan, Xiang-Cheng Chen, et al. Spectral baseline estimation using penalized least squares with weights derived from the Bayesian method. [J]. Nuclear Science and Techniques 33(11):148(2022) DOI： 10.1007/s41365-022-01132-9.

摘要

Abstract

The penalized least squares (PLS) method with appropriate weights has proven 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 ,86,Kr 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

Keywords

Penalized least squaresBaseline correctionBayesian ruleSpectrum analysis

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