Imaging neutron capture cross sections: i-TED proof-of-concept and future prospects based on Machine-Learning techniques

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Imaging neutron capture cross sections: i-TED proof-of-concept and future prospects based on Machine-Learning techniques

JournalArticle (Originalarbeit in einer wissenschaftlichen Zeitschrift)

ID	4623688
Author(s)	Babiano-Suarez, V.; Lerendegui-Marco, J.; Balibrea-Correa, J.; Caballero, L.; Calvo, D.; Ladarescu, I.; Real, D.; Domingo-Pardo, C.; Calvino, F.; Casanovas, A.; Tarifeno-Saldivia, A.; Alcayne, V.; Guerrero, C.; Millan-Callado, M. A.; Rodriguez-Gonzalez, T.; Barbagallo, M.; Aberle, O.; Amaducci, S.; Andrzejewski, J.; Audouin, L.; Bacak, M.; Bennett, S.; Berthoumieux, E.; Billowes, J.; Bosnar, D.; Brown, A.; Busso, M.; Caamano, M.; Calviani, M.; Cano-Ott, D.; Cerutti, F.; Chiaveri, E.; Colonna, N.; Cortes, G.; Cortes-Giraldo, M. A.; Cosentino, L.; Cristallo, S.; Damone, L. A.; Davies, P. J.; Diakaki, M.; Dietz, M.; Dressler, R.; Ducasse, Q.; Dupont, E.; Duran, I.; Eleme, Z.; Fernandez-Dominguez, B.; Ferrari, A.; Finocchiaro, P.; Furman, V.; Goebel, K.; Garg, R.; Gawlik, A.; Gilardoni, S.; Goncalves, I. F.; Gonzalez-Romero, E.; Gunsing, F.; Harada, H.; Heinitz, S.; Heyse, J.; Jenkins, D. G.; Junghans, A.; Kaeppeler, F.; Kadi, Y.; Kimura, A.; Knapova, I.; Kokkoris, M.; Kopatch, Y.; Krticka, M.; Kurtulgil, D.; Lederer-Woods, C.; Leeb, H.; Lonsdale, S. J.; Macina, D.; Manna, A.; Martinez, T.; Masi, A.; Massimi, C.; Mastinu, P.; Mastromarco, M.; Maugeri, E. A.; Mazzone, A.; Mendoza, E.; Mengoni, A.; Michalopoulou, V.; Milazzo, P. M.; Mingrone, F.; Moreno-Soto, J.; Musumarra, A.; Negret, A.; Ogallar, F.; Oprea, A.; Patronis, N.; Pavlik, A.; Perkowski, J.; Persanti, L.; Petrone, C.; Pirovano, E.; Porras, I.; Praena, J.; Quesada, J. M.; Ramos-Doval, D.; Rauscher, T.; Reifarth, R.; Rochman, D.; Rubbia, C.; Sabate-Gilarte, M.; Saxena, A.; Schillebeeckx, P.; Schumann, D.; Sekhar, A.; Smith, A. G.; Sosnin, N.; Sprung, P.; Stamatopoulos, A.; Tagliente, G.; Tain, J. L.; Tassan-Got, L.; Thomas, Th; Torres-Sanchez, P.; Tsinganis, A.; Ulrich, J.; Urlass, S.; Valenta, S.; Vannini, G.; Variale, V.; Vaz, P.; Ventura, A.; Vescovi, D.; Vlachoudis, V.; Vlastou, R.; Wallner, A.; Woods, P. J.; Wright, T.; Zugec, P.
Author(s) at UniBasel	Rauscher, Thomas
Year	2021
Title	Imaging neutron capture cross sections: i-TED proof-of-concept and future prospects based on Machine-Learning techniques
Journal	European Physical Journal A: Hadrons and Nuclei
Volume	57
Number	6
Pages / Article-Number	197
Abstract	i-TED is an innovative detection system which exploits Compton imaging techniques to achieve a superior signal-to-background ratio in (n,γ) cross-section measurements using time-of-flight technique. This work presents the first experimental validation of the i-TED apparatus for high-resolution time-of-flight experiments and demonstrates for the first time the concept proposed for background rejection. To this aim, the 197Au(n,γ) and 56Fe(n,γ) reactions were studied at CERN n_TOF using an i-TED demonstrator based on three position-sensitive detectors. Two C6D6 detectors were also used to benchmark the performance of i-TED. The i-TED prototype built for this study shows a factor of ∼3 higher detection sensitivity than state-of-the-art C6D6 detectors in the 10 keV neutron-energy region of astrophysical interest. This paper explores also the perspectives of further enhancement in performance attainable with the final i-TED array consisting of twenty position-sensitive detectors and new analysis methodologies based on Machine-Learning techniques.
Publisher	EDP Sciences with Springer and Società Italiana di Fisica
ISSN/ISBN	1434-6001 ; 1434-601X
edoc-URL	https://edoc.unibas.ch/84210/
Full Text on edoc	No
Digital Object Identifier DOI	10.1140/epja/s10050-021-00507-7
ISI-Number	000662881100001
Document type (ISI)	Article

29/04/2024

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