Efficient event generation is a major computational challenge for precision collider phenomenology, especially for high-multiplicity final states where matrix-element evaluations are expensive and rejection-sampling efficiencies are low. We study an alternative approach based on many parallel underdamped Langevin chains, retaining one terminal state from each chain to obtain unweighted events while avoiding within-chain autocorrelation. A learned Stein discrepancy is used as a convergence diagnostic, providing a data-driven estimate of the relaxation time. We apply the method to tree-level $u\bar u\to Z+n g$ event generation and find that relaxation requires only a modest number of exact-target Langevin steps, with mild growth over the multiplicities studied. Finally, we show that simple neural-network surrogate initialization can substantially reduce the required number of exact matrix-element and gradient evaluations.

翻译：高效事件生成是精确对撞现象学中的重大计算挑战，特别是对于高多重末态（其中矩阵元评估成本高昂且拒绝采样效率低下）的情况。我们研究了一种基于多个并行欠阻尼朗之万链的替代方法，从每条链中保留一个终态以获取无权重事件，同时避免链内自相关。采用学习型斯坦因差异作为收敛诊断指标，提供数据驱动的弛豫时间估计。我们将该方法应用于树图级$u\bar u\to Z+n g$事件生成，发现弛豫仅需适度数量的精确目标朗之万步骤，且该数量在所研究的多重性范围内增长平缓。最后，我们证明简单的神经网络代理初始化可大幅减少所需的精确矩阵元和梯度评估次数。