Speaker
Description
Neutron-induced reactions in which at least one of the reaction products is a light charged particle are of prime importance in a variety of fields: fundamental research, medical physics, nuclear applications, i.e., fusion reactors. In fusion reactors, special efforts are ongoing to determine threshold reactions of structural materials. These materials are exposed to extreme irradiation conditions, which can lead to the formation of helium or gas within them, altering their properties. In particular, for several commonly used structural materials of fusion reactors, these data sets are urgently needed.
This contribution is about the “proof-of-principle” of a novel charged particle detector setup by taking advantage of the unique characteristics of the neutron Time-Of-Flight facility at CERN. The setup consists of a silicon wafer with a CD shape, segmented on both sides into strips and sectors, offering position-sensitive measurements [4,5]. The silicon wafer has undergone the neutron transmutation doping technique, providing a uniform electric field inside the silicon wafer. The NTD feature allows the application of an in-house Pulse Shape Analysis (PSA) algorithm for discrimination by using Machine Learning (ML) algorithms.
Within this contribution, the characterization of the detector’s response and the experimental configuration will be presented along with the experimental results.