Abstract
There have been many studies which analyze complex alpha spectra based on numerically fitting the peaks to calculate the activity level of the sample. In the present work we propose a different approach – the application of neural network techniques to fit the peaks in alpha spectra. Instead of using a mathematical function to fit the peak, the fitting is done by a neural network trained with experimental data corresponding to peaks of different characteristics. We have designed a feed-forward (FF) multi-layer perceptron (MLP) artificial neural network (ANN), with supervised training based on a back-propagation (BP) algorithm, trained on the peaks of Polonium, extracted from many spectra of real samples analyzed in the laboratory. With this method, we have achieved a fitting procedure that does not introduce any error greater than the error of measurement, evaluated to be 10%.
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References
Blaauw, M., García-Toraño, E., Woods, S., et al.: The 1997 IAEA Intercomparison of Commercially Available PC-Based Software for Alpha-Particle Spectrometry. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 428, 317–329 (1999)
Bortels, G., Collaers, P.: Analytical Function for Fitting Peaks in Alpha-Particle Spectra from Si Detectors. International Journal of Radiation Applications and Instrumentation. Part A. Applied Radiation and Isotopes 38, 831–837 (1987)
Lozano, J.C., Fernández, F.: ALFIT: A Code for the Analysis of Low Statistic Alpha-Particle Spectra from Silicon Semiconductor Detectors. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 413, 357–366 (1998)
Martín Sánchez, A., Rubio Montero, P., Vera Tomé, F.: FITBOR: A New Program for the Analysis of Complex Alpha Spectra. Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 369, 593–596 (1996)
Miranda Carpintero, J., Pérez Utrero, R.: Universidad de Extremadura. Escuela Politécnica: Desarrollo del software básico de análisis en espectrometría alfa (2006)
Pommé, S., Sibbens, G.: A New Off-Line Gain Stabilisation Method Applied to Alpha-Particle Spectrometry. Advanced Mathematical and Computational Tools in Metrology VI, 327–329 (2004)
Westmeier, W., Van Aarle, J.: PC-Based High-Precision Nuclear Spectrometry. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 286, 439–442 (1990)
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Miranda, J., Pérez, R., Baeza, A., Guillén, J. (2009). Study of Alpha Peak Fitting by Techniques Based on Neural Networks. In: Palmer-Brown, D., Draganova, C., Pimenidis, E., Mouratidis, H. (eds) Engineering Applications of Neural Networks. EANN 2009. Communications in Computer and Information Science, vol 43. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03969-0_8
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DOI: https://doi.org/10.1007/978-3-642-03969-0_8
Publisher Name: Springer, Berlin, Heidelberg
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