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. AudouinM. 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

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    14 Citations (Scopus)

    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.

    Original languageEnglish
    Article number197
    JournalEuropean Physical Journal A
    Volume57
    Issue number6
    DOIs
    Publication statusPublished - Jun 2021

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