Multi-objective Bayesian optimization of eco-friendly gas mixtures for resistive plate chambers - Pit Bechtold; Dario Stocco; Christian M Franck (Machine Learning: Science and Technology, 2025)
Developing environmentally friendly gas mixtures for resistive plate chambers (RPCs) has been a research focus for the last few years. This study presents a novel approach for autonomous optimization of the detector performance and environmental impact of gas mixtures and enabling a more efficient evaluation for finding a suitable replacement for RPCs. The proposed framework is built upon multi-objective Bayesian optimization (BO) with Pareto front prediction and per-point noise implementation. This work fills a research gap by applying advanced optimization tools, specifically BO, to challenges in gaseous detectors, gas insulation systems and electrical engineering: An approach that is not commonly used in the field. This framework can be applied in both experimental and simulation studies, and is applied here in a study of CO _2 -based standard mixtures, intending to enhance avalanche-to-streamer separation and to reduce the gas mixture’s CO _2 equivalent (CO _2 e). The resulting optimization includes 259 simulated gas mixtures and indicates that reducing the environmental impact of CO _2 -based standard mixtures always comes at the cost of reducing the separation. No mixture with performance superior to or similar to the gas mixtures currently in use could be found while also significantly reducing the CO _2 e. This optimization yields a detailed simulated data set for gas mixture replacement in RPCs, showing a hypervolume improvement that exceeds traditional methods within 33% of the number of iterations, thus eliminating the need for time-consuming traditional parameter space sampling approaches. Future enhancements utilizing ultralow global warming potential gases, such as HFO-1234ze(E), or additional objectives, can be realized within this novel current framework.