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

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 i...

• Type: Journal
• Year: 2002
• Where: NCA
• Authors: Radwan E. Abdel-Aal
• Keywords: Computer Networks, Fitting Gaussian Peaks, NCA 2002, Peak Width, Single Pure Gaussian