论文标题
自动回归流量的加权事件的相空间采样和推断
Phase Space Sampling and Inference from Weighted Events with Autoregressive Flows
论文作者
论文摘要
我们探讨了自回旋流的使用，这是一种具有可拖动可能性的生成模型，作为有效生成物理粒子撞机事件的一种手段。通常的最大似然损失函数由事件重量补充，可以从具有可变的事件样本，甚至负面事件权重进行推断。为了说明该模型的功效，我们在具有重要性采样权重的电子对撞机上对领先的顶级生产事件进行实验，并在涉及负重负重的LHC的近级领先级顶对生产事件中进行实验。
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.
