Designing particle physics experiments with artificial intelligence

Le résumé fourni par la source

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.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.