arXiv · 2012.10374
Imaging neutron capture cross sections: i-TED proof-of-concept and future prospects based on Machine-Learning techniques
Abstract
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 both $^{197}$Au($n,\gamma$) and $^{56}$Fe($n, \gamma$) reactions were measured at CERN n\_TOF using an i-TED demonstrator based on only three position-sensitive detectors. Two \cds detectors were also used to benchmark the performance of i-TED. The i-TED prototype built for this study shows a factor of $\sim$3 higher detection sensitivity than state-of-the-art \cds detectors in the $\sim$10~keV neutron energy range 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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V. Babiano-Suárez, J. Lerendegui-Marco, J. Balibrea-Correa, L. Caballero, D. Calvo, I. Ladarescu, C. Domingo-Pardo, F. Calviño, A. Casanovas, A. Tarifeño-Saldivia, V. Alcayne, C. Guerrero, M. A. Millán-Callado, M. 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. Fern\', ez-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. 2020-12-18. Imaging neutron capture cross sections: i-TED proof-of-concept and future prospects based on Machine-Learning techniques. https://doi.org/10.1140/epja/s10050-021-00507-7
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