Simulations play a key role for inference in collider physics. We explore various approaches for enhancing the precision of simulations using machine learning, including interventions at the end of the simulation chain (reweighting), at the beginning of the simulation chain (pre-processing), and connections between the end and beginning (latent space refinement). To clearly illustrate our approaches, we use W+jets matrix element surrogate simulations based on normalizing flows as a prototypical example. First, weights in the data space are derived using machine learning classifiers. Then, we pull back the data-space weights to the latent space to produce unweighted examples and employ the Latent Space Refinement (LASER) protocol using Hamiltonian Monte Carlo. An alternative approach is an augmented normalizing flow, which allows for different dimensions in the latent and target spaces. These methods are studied for various pre-processing strategies, including a new and general method for massive particles at hadron colliders that is a tweak on the widely-used RAMBO-on-diet mapping. We find that modified simulations can achieve sub-percent precision across a wide range of phase space.

翻译：模拟在对撞机物理的推断中扮演关键角色。我们探索了多种利用机器学习提升模拟精度的途径，包括在模拟链末端（重加权）、模拟链开端（预处理）进行干预，以及连接末端与开端的方法（潜在空间精炼）。为清晰阐述这些方法，我们以基于归一化流的W+喷注矩阵元替代模拟作为典型示例。首先，利用机器学习分类器推导出数据空间中的权重；随后，将这些数据空间权重拉回潜在空间以生成无加权样本，并采用哈密顿蒙特卡洛的潜在空间精炼（LASER）协议。另一种备选方案是增强型归一化流，它允许潜在空间与目标空间具有不同维度。针对多种预处理策略研究了这些方法，包括一种针对强子对撞机中大质量粒子的新型通用方法——它对广泛应用的RAMBO-on-diet映射进行了微调。我们发现，经改进的模拟能在广泛的相空间范围内达到亚百分比精度。