Dataset for RESuM: A Rare Event Surrogate Model for Physics Detector Design

This dataset contains the training data for ICLR 2025 Spotlight Paper: RESuM: A Rare Event Surrogate Model for Physics Detector Design:

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 % 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.

本数据集为ICLR 2025 亮点论文《RESuM：面向物理探测器设计的稀有事件替代模型》的训练数据。无中微子双β衰变（Neutrinoless Double-Beta Decay, NLDBD）的实验发现将解答物理学中最核心的科学问题之一：为何宇宙中物质总量多于反物质？为最大化发现概率，NLDBD实验需优化探测器设计，以最小化背景事件污染探测器的概率。由于该概率本征值极低，设计优化要么需要开展成本高昂的模拟以生成足够的背景计数，要么需应对显著的方差问题。本研究将这一困境形式化为稀有事件设计（Rare Event Design, RED）问题：即当待最小化的设计指标本征值极小时，识别最优设计参数。随后，我们针对RED场景下的物理探测器设计优化任务，构建了稀有事件替代模型（RESuM）。RESuM基于预训练条件神经过程（Conditional Neural Process, CNP）模型，将额外先验知识融入多保真度高斯过程模型中。我们将RESuM应用于LEGEND NLDBD实验的中子屏蔽设计优化工作，最终得到了可将中子背景降低%、且计算资源消耗仅为传统方法3.3%的最优设计方案。鉴于RED问题在其他物理科学领域（尤其是稀有事件搜索方向）的普遍性，RESuM算法具备广泛的应用潜力，可加速计算模拟密集型相关任务。