APSO Tuning for Time-Efficient NIM Mass Spectrometer Optimisation Aboard the JUICE Spacecraft

The JUpiter ICy moons Explorer (JUICE) mission carries a suite of scientific instruments designed to investigate the Jovian system. Among them, the Neutral and Ion Mass spectrometer (NIM) is dedicated to measuring the composition of neutral and ionized particles in the spacecraft environment. NIM operates by applying a set of predefined voltages to its internal components to ionize, guide and analyse incoming particles. In nominal operation, this configuration consists of 22 voltage values, defined in a voltage set. The 19 voltages applied in the ion source and ion optics must be optimized to achieve the physically attainable sensitivity and mass resolution of the instrument. Subsequent steps involve adjustments to regulate molecular fragmentation effects induced by electron impact ionization within the ion source and to administer operational trade-offs between instrument performance and instrument lifetime. Therefore, some of these voltage optima vary from one instrument start-up to another, while others are primarily temperature or other environmental variables dependent. Still others are only dependent on the operational strategy or remain largely unchanged. Furthermore, variations as a function of the instrument operating hours must be expected. We investigate how sensitive signal intensity and mass resolution respond to variations of selected voltages. Based on these observations, conclusions are drawn to configure the implemented NIM Adaptive Particle Swarm Optimizer (APSO), which is used to optimize the instrument voltage configuration, automatically in flight, in a time-efficient and case-specific manner. Three scenarios are considered: a complete loss of signal with no valid voltage set available, the presence of preliminary knowledge of a usable voltage set with the intention to further explore the APSO search space, and rapid fine-tuning of an already well-performing voltage set. The tuneable parameters of the NIM APSO include the initial cognitive and social acceleration coefficients, as well as parameters to control their evolution, the global limitation of the evolutionary velocity, the choice of the generation to particle ratio, and the selected elitist learning strategy. The influence of tuning individual factors is examined, and the total time efficiency increase of APSO optimization is quantified.