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

Babiano-Suárez, V., Lerendegui-Marco, J., Balibrea-Correa, J., Caballero, L., Calvo, D., Ladarescu, I., Real, D., Domingo-Pardo, C., Calviño, F., Casanovas, A., Tarifeño-Saldivia, A., Alcayne, V., Guerrero, C., Millán-Callado, M. A., Rodríguez-González, 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., Caamaño, M., Calviani, M., Cano-Ott, D., Cerutti, F., Chiaveri, E., Colonna, N., Cortés, G., Cortés-Giraldo, M. A., Cosentino, L., Cristallo, S., Damone, L. A., Davies, P. J., Diakaki, M., Dietz, M., Dressler, R., Ducasse, Q., Dupont, E., Durán, I., Eleme, Z., Fernández-Domínguez, B., Ferrari, A., Finocchiaro, P. and Rauscher, T. (2021) Imaging neutron capture cross sections: i-TED proof-of-concept and future prospects based on Machine-Learning techniques. The European Physical Journal A (EPJ A), 57 (6): 197. ISSN 1434-6001
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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.

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