好得难以置信：为多喷注过程循环利用相空间点
Too good to go: Upcycling Phase-Space Points for Multijet Processes
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中文总结 AI 辅助
该研究针对多喷注过程相空间采样成本高的问题，提出循环利用相空间点的训练策略，可降低采样器适配成本，提升性能，适用于多种采样器，在LHC相关过程中验证有效。
中文摘要 AI 辅助
高维相空间的高效采样是蒙特卡洛事例发生器的主要挑战，因为对于高多重度末态，散射矩阵元的计算会变得计算成本高昂。本文提出一种训练策略，可显著降低采样器适配的成本，同时提供的采样器性能优于当前基准。该方法利用相空间的嵌套结构：(N+1)粒子相空间可分解为N粒子相空间与单粒子相空间。我们假设已存在对应N粒子相空间的高效采样器，这在QCD X+n-喷注过程的栈中是常见情况。通过将N粒子数据集扩充为(N+1)粒子数据集，我们获得的训练样本更接近被积函数，且在统计上大于均匀采样的样本。以采样充分的N粒子核心启动采样器的适配阶段，可加速学习完整(N+1)粒子相空间密度。由于扩充样本仅用作初始提议分布，精确的事例权重仍可保证无偏的蒙特卡洛估计。该方法对所训练的采样器无特定要求，可应用于基于机器学习的方法，也可用于VEGAS等更传统的算法。我们在大型强子对撞机（LHC）的喷注关联Drell-Yan及顶夸克对产生过程中，使用连续归一化流采样器，验证了该方法可减少矩阵元计算量并提升最终性能。
英文摘要
The efficient sampling of high-dimensional phase spaces is a major challenge for Monte Carlo event generators, as for high-multiplicity final states the evaluation of scattering matrix elements becomes computationally expensive. We here introduce a training strategy that significantly reduces the cost of adapting the samplers, while delivering samplers that outperform the current benchmarks. The method exploits the nested structure of phase spaces, where an $(N+1)$-particle phase space factorises into an $N$-particle and a one-particle phase space. We thereby assume that an efficient sampler for the corresponding $N$-particle phase space is already available, as is the case in stacks of QCD $X+n$-jets processes. By augmenting an $N$-particle to an $(N+1)$-particle dataset, we obtain a training sample that more closely resembles the integrand and is statistically larger than, for example, a uniformly sampled one. Starting the adaptation phase of the sampler with a well-sampled $N$-particle core accelerates learning of the full $(N+1)$-particle phase-space density. Since the augmented sample is only used as an initial proposal distribution, unbiased Monte Carlo estimates are still guaranteed by exact event weighting. The approach is agnostic to the trained sampler and can be applied to machine-learning-based methods as well as more traditional algorithms such as VEGAS. We demonstrate the reduction in matrix-element evaluations and final performance increase for jet-associated Drell--Yan and top-pair production at the LHC, using Continuous Normalising Flow samplers.
