AI 中文总结
研究利用神经控制变量解决相空间积分和事件生成问题，通过构建有符号控制变量结合神经重要性采样降低计算成本，高阶时条件神经控制变量可作可训练减法项提升采样性能。
AI 中文摘要
我们采用神经控制变量来最小化事件权重范围并避免相空间积分和事件生成中的负权重。由两个归一化流构建的有符号控制变量可完成这两项任务。结合神经重要性采样，它显著降低了低阶和高阶预测的计算成本。对于高阶情况，我们的条件神经控制变量可视为可训练的减法项，补充既定物理减法方案以提高采样性能。
英文摘要
We employ neural control variates to minimize the range of event weights and avoid negative weights for phase-space integration and event generation. A signed control variate, built from two normalizing flows, fulfills both tasks. Combined with neural importance sampling, it significantly reduces the computational cost of LO and NLO predictions. For the NLO case, our conditional neural control variate can be viewed as a trainable subtraction term, complementing the established physics subtraction schemes for enhanced sampling performance.
Comments34 pages, 5 figures
