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

Babiano-Su??rez, V.; Lerendegui-Marco, J.; Balibrea-Correa, J.; Caballero, L.; Calvo, D.; Ladarescu, I.; Real, D.; Domingo-Pardo, C.; Calvi??o, F.; Casanovas, A.; Tarife??o-Saldivia, A.; Alcayne, V.; Guerrero, C.; Mill??n-Callado, M. A.; Rodr??guez-Gonz??lez, 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.; Caama??o, M.; Calviani, M.; Cano-Ott, D.; Cerutti, F.; Chiaveri, E.; Colonna, N.; Cort??s, G.; Cort??s-Giraldo, M. A.; Cosentino, L.; Cristallo, S.; Damone, L. A.; Davies, P. J.; Diakaki, M.; Dietz, M.; Dressler, R.; Ducasse, Q.; Dupont, E.; Dur??n, I.; Eleme, Z.; Fern??ndez-Dom??nguez, B.; Ferrari, A.; Finocchiaro, P.; Furman, V.; G??bel, K.; Garg, R.; Gawlik, A.; Gilardoni, S.; Gon??alves, I. F.; Gonz??lez-Romero, E.; Gunsing, F.; Harada, H.; Heinitz, S.; Heyse, J.; Jenkins, D. G.; Junghans, A.; K??ppeler, F.; Kadi, Y.; Kimura, A.; Knapova, I.; Kokkoris, M.; Kopatch, Y.; Krti??ka, 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.; Og??llar, 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.; Sabat??-Gilarte, M.; Saxena, A.; Schillebeeckx, P.; Schumann, D.; Sekhar, A.; Smith, A. G.; Sosnin, N. V.; Sprung, P.; Stamatopoulos, A.; Tagliente, G.; Tain, J. L.; Tassan-Got, L.; Thomas, Th.; Torres-S??nchez, 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.; ??ugec, P.

doi:10.1140/epja/s10050-021-00507-7

i-TED is an innovative detection system which exploits Compton imaging techniques to achieve a superior signal-to-background ratio in (n, gamma) 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 Au-197(n, gamma) and Fe-56(n, gamma) 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 similar to 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 newanalysis methodologies based on Machine-Learning techniques.