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Comparison of Algorithmic and Machine Learning Approaches for the Automatic Fitting of Gaussian Peaks

Summary: Comparison of Algorithmic and Machine Learning
Approaches for the Automatic Fitting of Gaussian Peaks
R. E. Abdel-Aal
Center for Applied Physical Sciences, Research Institute,
King Fahd University of Petroleum and Minerals,
Dhahran 31261, Saudi Arabia
Fitting gaussian peaks to experimental data is important in many disciplines, including
nuclear spectroscopy. Nonlinear least squares fitting methods have been in use for a long
time, but these are iterative, computationally intensive, and require user intervention.
Machine learning approaches automate and speed up the fitting procedure. However, for a
single pure gaussian, there exists a simple and automatic analytical approach based on
linearization followed by a weighted linear least squares (LS) fit. This paper compares this
algorithmic method with an abductive machine learning approach based on AIM (Abductory
Induction Mechanism). Both techniques are briefly described and their performance
compared for analysing simulated and actual spectral peaks. Evaluated on 500 peaks with
statistical uncertainties corresponding to a peak count of 100, average absolute errors for the
peak height, position, and width are 4.9%, 2.9%, and 4.2% for AIM versus 3.3%, 0.5%, and
7.7% for the LS. AIM is better for the width, while LS is more accurate for the position. LS
errors are more biased, underestimating the peak position and overestimating the peak


Source: Abdel-Aal, Radwan E. - Computer Engineering Department, King Fahd University of Petroleum and Minerals


Collections: Computer Technologies and Information Sciences; Power Transmission, Distribution and Plants