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

V. Babiano-Suárez,J. Lerendegui-Marco,J. Balibrea-Correa,L. Caballero,D. Calvo,I. Ladarescu,D. Real,C. Domingo-Pardo,F. Calviño,A. Casanovas,A. Tarifeño-Saldivia,V. Alcayne,C. Guerrero,M. A. Millán-Callado,T. Rodríguez-González,M. Barbagallo,O. Aberle,S. Amaducci,J. Andrzejewski, L. Audouin,M. Bacak,S. Bennett,E. Berthoumieux,J. Billowes,D. Bosnar,A. Brown,M. Busso,M. Caamaño,M. Calviani,D. Cano-Ott,F. Cerutti,E. Chiaveri,N. Colonna,G. Cortés,M. A. Cortés-Giraldo,L. Cosentino, S. Cristallo,L. A. Damone,P. J. Davies,M. Diakaki,M. Dietz,R. Dressler,Q. Ducasse,E. Dupont,I. Durán, Z. Eleme,B. Fernández-Domínguez,A. Ferrari,P. Finocchiaro,V. Furman,K. Göbel,R. Garg,A. Gawlik,S. Gilardoni,I. F. Gonçalves,E. González-Romero,F. Gunsing,H. Harada,S. Heinitz,J. Heyse,D. G. Jenkins,A. Junghans,F. Käppeler,Y. Kadi,A. Kimura,I. Knapova,M. Kokkoris,Y. Kopatch,M. Krtička,D. Kurtulgil,C. Lederer-Woods,H. Leeb,S. J. Lonsdale,D. Macina,A. Manna,T. Martinez,A. Masi,C. Massimi,P. Mastinu,M. Mastromarco,E. A. Maugeri,A. Mazzone,E. Mendoza,A. Mengoni, V. Michalopoulou,P. M. Milazzo,F. Mingrone, J. Moreno-Soto,A. Musumarra,A. Negret,F. Ogállar,A. Oprea,N. Patronis,A. Pavlik,J. Perkowski, L. Persanti,C. Petrone,E. Pirovano,I. Porras,J. Praena,J. M. Quesada, D. Ramos-Doval,T. Rauscher,R. Reifarth,D. Rochman,C. Rubbia,M. Sabaté-Gilarte,A. Saxena,P. Schillebeeckx,D. Schumann,A. Sekhar,A. G. Smith,N. V. Sosnin, P. Sprung,A. Stamatopoulos,G. Tagliente,J. L. Tain,L. Tassan-Got,Th. Thomas,P. Torres-Sánchez,A. Tsinganis,J. Ulrich, S. Urlass,S. Valenta,G. Vannini,V. Variale,P. Vaz,A. Ventura,D. Vescovi,V. Vlachoudis,R. Vlastou,A. Wallner,P. J. Woods,T. Wright,P. Žugec

The European Physical Journal A(2021)

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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 ^197 Au( n,γ ) and ^56 Fe( n, γ ) reactions were studied at CERN n_TOF using an i-TED demonstrator based on three position-sensitive detectors. Two C _6 D _6 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 C _6 D _6 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.
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Key words
neutron,imaging,machine-learning machine-learning,machine-learning machine-learning techniques,capture,i-ted,proof-of-concept
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