RESuM: A Rare Event Surrogate Model for Physics Detector Design

Ann-Kathrin Schuetz, A.W.P. Poon, Aobo Li

Keywords: surrogate model, simulation, rare event search, AI4Sci, AI for physics, conditional neural process, Bayesian methods, emulator, Multi-Fidelity Gaussian Process

TL;DR: We developed a Rare Event Surrogate Model (RESuM) powered by Conditional Neural Process to optimize particle physics detector design under high-variance design metrics.

Abstract: The experimental discovery of neutrinoless double-beta decay (NLDBD) would answer one of the most important questions in physics: Why is there more matter than antimatter in our universe? To maximize the chances of discovery, NLDBD experiments must optimize their detector designs to minimize the probability of background events contaminating the detector. Given that this probability is inherently low, design optimization either requires extremely costly simulations to generate sufficient background counts or contending with significant variance. In this work, we formalize this dilemma as a Rare Event Design (RED) problem: identifying optimal design parameters when the design metric to be minimized is inherently small. We then designed the Rare Event Surrogate Model (RESuM) for physics detector design optimization under RED conditions. RESuM uses a pre-trained Conditional Neural Process (CNP) model to incorporate additional prior knowledge into a Multi-Fidelity Gaussian Process model. We applied RESuM to optimize neutron shielding designs for the LEGEND NLDBD experiment, identifying an optimal design that reduces the neutron background by $(66.5 \pm 3.5)$% while using only 3.3% of the computational resources compared to traditional methods. Given the prevalence of RED problems in other fields of physical sciences, especially in rare-event searches, the RESuM algorithm has broad potential for accelerating simulation-intensive applications.

Primary Area: applications to physical sciences (physics, chemistry, biology, etc.)

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Submission Number: 4920