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Fast adaptive particle spectrum fitting algorithm based on moment-estimated initial parameters.

Rui Shi1, Xianguo Tuo1, Honglong Zheng2

  • 1Fundamental Science on Nuclear Wastes and Environmental Safety Laboratory, Southwest University of Science and Technology, Mianyang, Sichuan, China; Sichuan University of Science & Engineering, Zigong, Sichuan, China; State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu, Sichuan, China.

Applied Radiation and Isotopes : Including Data, Instrumentation and Methods for Use in Agriculture, Industry and Medicine
|August 4, 2017
PubMed
Summary
This summary is machine-generated.

A new moment estimation algorithm provides initial parameters for particle spectrum peak fitting. This method enhances automated spectral analysis and ensures optimized fitting for gamma and alpha spectra.

Keywords:
Initial parametersMoment estimationParticle spectrumPeak shape function

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Area of Science:

  • Nuclear Physics
  • Spectroscopy
  • Computational Science

Background:

  • Accurate peak shape function parameter estimation is crucial for spectral analysis.
  • Iteration fitting procedures often require reliable initial parameters for convergence.
  • Existing methods may lack efficiency or robustness in determining these initial values.

Purpose of the Study:

  • To develop an algorithm for determining initial parameters of particle spectrum peak shape functions.
  • To improve the efficiency and reliability of iteration fitting procedures in spectral analysis.
  • To provide a robust method for automated peak curve fitting.

Main Methods:

  • An algorithm based on moment estimation (mean, variance, third-order central moment) was developed.
  • The algorithm utilizes spectrum peak data to calculate these moments.
  • The calculated moments are used to solve for the initial parameters of the fitting function, which are then fed into the Levenberg-Marquardt algorithm.

Main Results:

  • The proposed moment estimation algorithm successfully determined initial parameters for peak shape functions.
  • The algorithm demonstrated effectiveness when tested with both gamma and alpha spectra.
  • The use of these initial parameters ensured convergence and optimized fitting in the iteration procedure.

Conclusions:

  • The moment estimation algorithm is an effective tool for providing initial parameters in spectral peak fitting.
  • This method facilitates automated peak curve fitting and spectral analysis.
  • The algorithm offers a reliable approach for enhancing the performance of fitting procedures in various spectroscopic applications.