https://doi.org/10.1051/epjconf/202226010002
Compton Imaging and Machine-Learning techniques for an enhanced sensitivity in key stellar (n,γ) measurements
1 Instituto de Física Corpuscular, CSIC-University of Valencia, Spain
2 Universitat Politecnica de Catalunya (UPC), Barcelona, Spain
3 Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (CIEMAT), Madrid, Spain.
4 Dpto. Física Atómica, Molecular y Nuclear, Universidad de Sevilla, Sevilla, Spain
5 Centro Nacional de Aceleradores(CNA), Sevilla, Spain
6 European Organization for Nuclear Research (CERN), Meyrin, Switzerland
7 Instituto Nazionale di Fisica Nucleare, Sezione di Bari, Italy.
8 Paul Scherrer Institut (PSI), Villigen, Switzerland
9 Institut Laue-Langevin ILL, Grenoble, France
* e-mail: jorge.lerendegui@ific.uv.es
Published online: 24 February 2022
Neutron capture cross-section measurements are fundamental in the study of astrophysical phenomena, such as the slow neutron capture (s-) process of nucleosynthesis operating in red-giant stars. To enhance the sensitivity of such measurements we have developed the i-TED detector. i-TED is an innovative detection system which exploits the Compton imaging technique with the aim of obtaining information about the incoming direction of the detected γ-rays. The imaging capability allows one to reject a large fraction of the dominant γ-ray background, hence enhancing the (n,γ) detection sensitivity.
This work summarizes the main 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 C6D6 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 C6D6 detectors in the ~10 keV neutron-energy range of astrophysical interest. This works also introduces the perspectives of further enhancement in performance attainable with the final i-TED array 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. Finally, we present our proposal to use this detection system for the first time on key astrophysical (n,γ) measurements, in particular on the s-process branching-point 79Se, which is especially well suited to constrain the thermal conditions of Red Giant and Massive Stars.
© The Authors, published by EDP Sciences, 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
