Machine learning aided neutron yield for dud detection based on JET and TFTR deuterium–tritium plasmas

Abstract In future fusion reactors, measuring the neutron production rate from deuterium–tritium (DT) fusion reactions will be crucial as it indicates fusion performance. To optimize Tritium consumption and limit neutron activation, a support function included in the plasma control system called a dud detector will trigger an alarm if the plasma fails to achieve expected fusion performance. This function has been developed and routinely employed at Joint European Torus during DT campaigns. This study presents machine-learning methods based on a surrogate model of the neutron rate, which can be used as an advanced dud detector. In preparation for DT operations in BEST, HL-3, ITER and SPARC, we investigate the portability and inherent limitations of these machine learning methods by analyzing similar DT experiments conducted at Tokamak Fusion Test Reactor.