English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Diagnostics are critical on the path to commercial fusion reactors, sincemeasurements and characterisation of the plasma is important for sustainingfusion reactions. Gamma spectroscopy is commonly used to provide informationabout the neutron energy spectrum from activation analysis, which can be usedto calculate the neutron flux and fusion power. The detection limits formeasuring nuclear dosimetry reactions used in such diagnostics arefundamentally related to Compton scattering events making up a backgroundcontinuum in measured spectra. This background lies in the same energy regionas peaks from low-energy gamma rays, leading to detection and characterisationlimitations. This paper presents a digital machine learning Compton suppressionalgorithm (MLCSA), that uses state-of-the-art machine learning techniques toperform pulse shape discrimination (PSD) for high purity germanium (HPGe)detectors. The MLCSA identifies key features of individual pulses todifferentiate between those that are generated from photopeaks and Comptonscatter events. Compton events are then rejected, reducing the low energybackground. This novel suppression algorithm improves gamma spectroscopyresults by lowering minimum detectable activity (MDA) limits and thus reducingthe measurement time required to reach the desired detection limit. In thispaper, 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) improvedby 49%, while the Co-60 peaks were partially preserved (reduced by 78%). TheMLCSA requires no modelling of the specific detector and so has the potentialto be detector agnostic, meaning the technique could be applied to a variety ofdetector types and applications.

Machine Learning Based Compton Suppression for Nuclear Fusion Plasma Diagnostics