Designing particle physics experiments with artificial intelligence

The design of modern particle physics detectors can become a strong benchmark for contemporary machine-learning techniques. It offers a realistic large-scale optimization task grounded in well-understood physics and reliable simulations, providing a controlled setting to test methods aimed at complex real-world problems; the proposed Future Circular Collider is a prominent example of the scale and ambition involved. This review introduces the detector-optimization problem and discusses the growing interest in applying AI methods to detector design, providing a comparative perspective on various methodologies. We show how a specific version of the detector-optimization problem can, and has been, tackled with Bayesian optimization and gradient-based methods, while reinforcement learning addresses a more general formulation that includes sequential and combinatorial structure. The substantial computational burden of Monte Carlo simulation remains a central obstacle, for which we outline how generative machine-learning approaches offer effective mitigation. We also discuss how uncertainty, arising from stochastic detector response, systematic shifts in physics modelling and reconstruction, and long-term operating conditions, can be incorporated into the design process. In particular, we discuss how distributional and distributionally robust reinforcement learning, together with optimal-transport–based ambiguity sets, provides a principled way to capture plausible deviations from nominal assumptions and to search for designs that maintain reliable performance across varied scenarios.