We explore the use of autoregressive flows, a type of generative model with tractable likelihood, as a means of efficient generation of physical particle collider events. The usual maximum likelihood loss function is supplemented by an event weight, allowing for inference from event samples with variable, and even negative event weights. To illustrate the efficacy of the model, we perform experiments with leading-order top pair production events at an electron collider with importance sampling weights, and with next-to-leading-order top pair production events at the LHC that involve negative weights.

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相空间采样和具有自回归流的加权事件的推断

我们探索使用自回归流（一种具有可预测可能性的生成模型）作为有效生成物理粒子对撞机事件的手段。通常的最大似然损失函数由事件权重进行补充，允许从具有可变甚至负事件权重的事件样本进行推断。为了说明该模型的有效性，我们在具有重要采样权重的电子对撞机上进行前导顶级对生产事件的实验，并在LHC上进行涉及负权重的次于前对顶级对生产事件的实验。