i-TED: Compton Imaging and Machine-Learning Techniques for Enhanced Sensitivity Neutron Capture Time-of-flight Measurements

i-TED is an innovative detection system which exploits Compton imaging techniques with the goal of achieving a superior signal-to-background ratio in time-of-flight neutron capture cross-section measurements. This work provides first an overview of the results of the first experimental proof-of-concept of the background rejection with i-TED carried out at CERN n_TOF using an early i-TED demonstrator. Two state-of-the-art C 6 D 6 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 C 6 D 6 detectors in the ~10 keV neutron-energy range of astrophysical interest. This contribution 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. The latter provide higher (n,γ) detection efficiency and similar enhancement in the sensitivity than the analytical method based on the Compton scattering law. The performance of this device has been further improved after the latest upgrades in Compton imaging algorithms, which are also presented in this contribution.