i-TED is an innovative detection system which exploits Compton imaging techniques to achieve a superior signal-to-background ratio in () 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 Au() and Fe() reactions were studied at CERN n_TOF using an i-TED demonstrator based on three position-sensitive detectors. Two CD 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 CD 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.
Imaging neutron capture cross sections: i-TED proof-of-concept and future prospects based on Machine-Learning techniques
Mazzone A;
2021
Abstract
i-TED is an innovative detection system which exploits Compton imaging techniques to achieve a superior signal-to-background ratio in () 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 Au() and Fe() reactions were studied at CERN n_TOF using an i-TED demonstrator based on three position-sensitive detectors. Two CD 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 CD 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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.