论文标题
MADNIS-神经多通道重要性抽样
MadNIS -- Neural Multi-Channel Importance Sampling
论文作者
论文摘要
LHC的理论预测需要精确的数值相位空间集成和未加权事件的产生。我们将机器学习的多通道权重与用于重要性采样的正常流量相结合，以改善经典的数值集成方法。我们基于可逆网络开发了有效的双向设置，将在线和缓冲培训结合了潜在昂贵的集成量。我们以额外的狭窄共振来说明我们的Drell-Yan过程方法。
Theory predictions for the LHC require precise numerical phase-space integration and generation of unweighted events. We combine machine-learned multi-channel weights with a normalizing flow for importance sampling, to improve classical methods for numerical integration. We develop an efficient bi-directional setup based on an invertible network, combining online and buffered training for potentially expensive integrands. We illustrate our method for the Drell-Yan process with an additional narrow resonance.
