Speakers
Description
Radiative neutron-capture cross sections are of pivotal importance in many fields such as nucleosynthesis studies or innovative reactor technologies. A large number of isotopes have been measured with high accuracy, but there are still a large number of relevant isotopes whose cross sections could not be experimentally determined yet, at least with sufficient accuracy and completeness, owing to limitations in detection techniques, sample production methods or in the facilities themselves.
In the context of the HYMNS (High-sensitivitY Measurements of key stellar Nucleo-Synthesis reactions) project [1] over the last five years we have developed a novel detection technique aimed at background suppression in radiative TOF neutron capture cross-section measurements. This new technique utilizes the latest position-sensitive photon-detection technology in combination with advanced Machine-Learning algorithms, both of them optimized for the harsh background conditions of the neutron laboratory, in order to enhance the signal-to-background by means of Compton imaging. The latter allows efficient distinction between true capture events arising from the sample under study and contaminant background events from the surroundings.
A summary on the main results [2-3] of this project will be given in this contribution together with an update on forthcoming experiments at CERN n_TOF and an outlook on future development steps.
[1] Project funded by the European Research Council under ERC grant agreement Nr. 681740
[2] V. Babiano-Suarez et al., The European Physical Journal A, Volume 57, Issue 6, article id.197 (2021)
[3] V. Babiano et al., Nuclear Inst. and Methods in Physics Research, A, Volume 953, article id. 163228 (2020)
