Babiano-Suarez, V, Lerendegui-Marco, J, Balibrea-Correa, J, Caballero, L, Calvo, D, Ladarescu, I, Real, D, Domingo-Pardo, C, Calvino, F, Casanovas, A, Tarifeno-Saldivia, A, Alcayne, V, Guerrero, C, Millan-Callado, MA, Rodriguez-Gonzalez, 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, Caamano, M, Calviani, M, Cano-Ott, D, Cerutti, F, Chiaveri, E, Colonna, N, Cortes, G, Cortes-Giraldo, MA, Cosentino, L, Cristallo, S, Damone, LA, Davies, PJ, Diakaki, M, Dietz, M, Garg, R, Jenkins, DG, Martinez, T, Saxena, A, Sekhar, A
, Smith, AG, Sosnin, NV, Woods, PJ
, Wright, T & The n_TOF Collaboration 2021, '
Imaging neutron capture cross sections: i-TED proof-of-concept and future prospects based on Machine-Learning techniques',
European Physical Journal A. Hadrons and Nuclei, vol. 57, pp. 197.
https://doi.org/10.1140/epja/s10050-021-00507-7