Pulse shape discrimination, to distinguish between neutrons and gamma rays, is a very important classification task in thermonuclear fusion. Gaussian Mixture Models and probabilistic Support Vector Machines have been applied to hundreds of thousands of pulses obtained with a counter based on the NE213 liquid scintillator. The results of the two completely independent mathematical methods are in very good agreement, the maximum discrepancy being of the order of 2%. The achieved classification also shows an excellent value for the figure of merit, a Mahalanobis type of distance, implemented to quantify statistically the separation between the two particle distributions. These two machine learning tools provide also the probability of each example being a neutron or a gamma ray, allowing more detailed studies of the distribution of pulses. The proposed methodology therefore clearly outperforms previous techniques in practically all aspects of the classification.

Gelfusa, M., Rossi, R., Lungaroni, M., Belli, F., Spolladore, L., Wyss, I., et al. (2020). Advanced pulse shape discrimination via machine learning for applications in thermonuclear fusion. NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH. SECTION A, ACCELERATORS, SPECTROMETERS, DETECTORS AND ASSOCIATED EQUIPMENT, 974, 164198 [10.1016/j.nima.2020.164198].

Advanced pulse shape discrimination via machine learning for applications in thermonuclear fusion

Gelfusa M.
Project Administration
;
Rossi R.
Formal Analysis
;
Lungaroni M.
Validation
;
Gaudio P.
Funding Acquisition
;
2020-01-01

Abstract

Pulse shape discrimination, to distinguish between neutrons and gamma rays, is a very important classification task in thermonuclear fusion. Gaussian Mixture Models and probabilistic Support Vector Machines have been applied to hundreds of thousands of pulses obtained with a counter based on the NE213 liquid scintillator. The results of the two completely independent mathematical methods are in very good agreement, the maximum discrepancy being of the order of 2%. The achieved classification also shows an excellent value for the figure of merit, a Mahalanobis type of distance, implemented to quantify statistically the separation between the two particle distributions. These two machine learning tools provide also the probability of each example being a neutron or a gamma ray, allowing more detailed studies of the distribution of pulses. The proposed methodology therefore clearly outperforms previous techniques in practically all aspects of the classification.
2020
Pubblicato
Rilevanza internazionale
Articolo
Esperti anonimi
Settore FIS/01 - FISICA SPERIMENTALE
Settore ING-IND/18 - FISICA DEI REATTORI NUCLEARI
English
Con Impact Factor ISI
Pulse shape discrimination Thermonuclear fusion Neutrons Gamma rays Gaussian Mixture Models Support Vector Machines
Gelfusa, M., Rossi, R., Lungaroni, M., Belli, F., Spolladore, L., Wyss, I., et al. (2020). Advanced pulse shape discrimination via machine learning for applications in thermonuclear fusion. NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH. SECTION A, ACCELERATORS, SPECTROMETERS, DETECTORS AND ASSOCIATED EQUIPMENT, 974, 164198 [10.1016/j.nima.2020.164198].
Gelfusa, M; Rossi, R; Lungaroni, M; Belli, F; Spolladore, L; Wyss, I; Gaudio, P; Murari, A
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2108/250183
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