论文标题
负重负重的蒙特卡洛事件的阳性重采样器
A Positive Resampler for Monte Carlo Events with Negative Weights
论文作者
论文摘要
我们提出了一个正面采样器，以解决与黑龙山脉散射过程中的事件样本相关的问题，通常涉及大量造成负重的大量事件。所提出的方法保证了所有物理分布的正权重，以及对所有可观察物的正确描述。该方法的理想侧面产品是可以减少通用事件生成器产生的事件样本的大小，从而降低了随后计算密集型事件处理步骤的资源需求。我们通过考虑将其应用于近距离订单 + Parton Shower合并预测与多种喷气机联合生产的$ W $ Boson的预测来证明我们方法的生存能力和效率。
We propose the Positive Resampler to solve the problem associated with event samples from state-of-the-art predictions for scattering processes at hadron colliders typically involving a sizeable number of events contributing with negative weight. The proposed method guarantees positive weights for all physical distributions, and a correct description of all observables. A desirable side product of the method is the possibility to reduce the size of event samples produced by General Purpose Event Generators, thus lowering the resource demands for subsequent computing-intensive event processing steps. We demonstrate the viability and efficiency of our approach by considering its application to a next-to-leading order + parton shower merged prediction for the production of a $W$ boson in association with multiple jets.
