Search Results for author: E. Chiaveri

Found 2 papers, 0 papers with code

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

no code implementations18 Dec 2020 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, 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

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.

Nuclear Experiment Instrumentation and Methods for Astrophysics

Machine learning based event classification for the energy-differential measurement of the $^\text{nat}$C(n,p) and $^\text{nat}$C(n,d) reactions

no code implementations11 Apr 2022 P. Žugec, M. Barbagallo, J. Andrzejewski, J. Perkowski, N. Colonna, D. Bosnar, A. Gawlik, M. Sabate-Gilarte, M. Bacak, F. Mingrone, E. Chiaveri

The paper explores the feasibility of using machine learning techniques, in particular neural networks, for classification of the experimental data from the joint $^\text{nat}$C(n, p) and $^\text{nat}$C(n, d) reaction cross section measurement from the neutron time of flight facility n_TOF at CERN.

Cannot find the paper you are looking for? You can Submit a new open access paper.