Machine Learning for Improvements to Gamma Spectroscopy in Nuclear Fusion Diagnostics

Fusion diagnostics are critical on the path to commercial fusion reactors, since the ability to understand and measure plasma features is important to sustaining fusion reactions. Gamma spectroscopy is one technique used to aid fusion diagnostics, to provide information on ion distribution and also in neutron activation analysis to calculate fusion power. However, a common feature with gamma spectroscopy is Compton scattering events within the detector. These elevate the background, reducing the likelihood of detecting peaks from low-energy gamma rays, leading to higher detection and characterisation limitations.

We present the groundwork for a digital Compton suppression algorithm that uses state-of-the-art machine learning techniques to perform Pulse Shape Discrimination. The algorithm identifies key pulse features to differentiate which are generated from photopeaks and Compton scatter events. Compton events are then rejected, reducing the low energy background.

This novel suppression algorithm improves gamma spectroscopy results by lowering detection limits and reducing measurement times. This will have positive implications on any area that uses gamma spectroscopy, including fusion diagnostic methods. It also has the potential to be detector agnostic, which will increase its applications.