  Diagnostics are critical on the path to commercial fusion reactors, since
measurements and characterisation of the plasma is important for sustaining
fusion reactions. Gamma spectroscopy is commonly used to provide information
about the neutron energy spectrum from activation analysis, which can be used
to calculate the neutron flux and fusion power. The detection limits for
measuring nuclear dosimetry reactions used in such diagnostics are
fundamentally related to Compton scattering events making up a background
continuum in measured spectra. This background lies in the same energy region
as peaks from low-energy gamma rays, leading to detection and characterisation
limitations. This paper presents a digital machine learning Compton suppression
algorithm (MLCSA), that uses state-of-the-art machine learning techniques to
perform pulse shape discrimination (PSD) for high purity germanium (HPGe)
detectors. The MLCSA identifies key features of individual pulses to
differentiate between those that 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 minimum detectable activity (MDA) limits and thus reducing
the measurement time required to reach the desired detection limit. In this
paper, the performance of the MLCSA is demonstrated using an HPGe detector,
with a gamma spectrum containing americium-241 (Am-241) and cobalt-60 (Co-60).
The MDA of Am-241 improved by 51% and the signal to noise ratio (SNR) improved
by 49%, while the Co-60 peaks were partially preserved (reduced by 78%). The
MLCSA requires no modelling of the specific detector and so has the potential
to be detector agnostic, meaning the technique could be applied to a variety of
detector types and applications.
