用神经网络在有限数据下修正高能物理模拟的多维偏差
Learning Minimal-Deviation Corrections for Multi-Dimensional Mismodelling in HEP Simulations
- 基于最小偏离原则，学习模拟事件的变换以匹配一维目标分布
- 在仅知一维数据时仍保持多维相关结构一致，提升模拟精度
- 适合高维复杂分析，尤其传统方法失效的场景
高能物理中的蒙特卡洛（MC）建模在复杂场景下极具挑战性，尤其当模拟无法复现观测数据时。实际中，实验信息通常仅限于一维分布，而偏差出现在多维特征空间中，这限制了传统修正方法：一维重加权忽略相关性，全维度方法又需大量目标数据。本文提出一种基于神经网络的方法，在此约束下学习模拟事件的变换，使其重现已知的一维目标分布，同时尽可能贴近原始模拟。该最小偏离原则在保留基准模型全局相关结构的同时，实现对偏差特征的精准修正。通过使用伪数据的受控实验，验证了该方法能有效改善与目标分布的一致性，并维持多维结构的稳定性。该方法适用于复杂高维分析，为信息有限下的MC建模提供可扩展的增强方案。
原文摘要 · Abstract (English)
Accurate Monte Carlo (MC) modelling in high-energy physics is challenging, particularly in complex scenarios where simulations fail to reproduce observed data. In practice, experimental information is often limited to one-dimensional (1D) distributions, while mismodelling arises in a multidimensional feature space. This restricts traditional correction methods, as one-dimensional reweighting ignores correlations and fully multidimensional approaches require large target datasets. We propose a neural network-based method that operates under these constraints by learning a transformation of simulated events that reproduces the available 1D target distributions while remaining close to the original simulation. This minimal-deviation principle preserves the global correlation structure of the baseline model while enabling targeted corrections of mismodelled features. Using controlled studies with simulated pseudo-data, we show that the method improves agreement with target distributions and maintains a consistent multidimensional structure. The approach is designed for complex, high-dimensional analyses where traditional techniques are insufficient, providing a scalable way to enhance MC modelling under limited information.
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