In this work we introduce $\nu^2$-Flows, an extension of the $\nu$-Flows method to final states containing multiple neutrinos. The architecture can natively scale for all combinations of object types and multiplicities in the final state for any desired neutrino multiplicities. In $t\bar{t}$ dilepton events, the momenta of both neutrinos and correlations between them are reconstructed more accurately than when using the most popular standard analytical techniques, and solutions are found for all events. Inference time is significantly faster than competing methods, and can be reduced further by evaluating in parallel on graphics processing units. We apply $\nu^2$-Flows to $t\bar{t}$ dilepton events and show that the per-bin uncertainties in unfolded distributions is much closer to the limit of performance set by perfect neutrino reconstruction than standard techniques. For the chosen double differential observables $\nu^2$-Flows results in improved statistical precision for each bin by a factor of 1.5 to 2 in comparison to the Neutrino Weighting method and up to a factor of four in comparison to the Ellipse approach.

翻译：本文提出$ν^2$-Flows方法，作为$ν$-Flows方法在包含多个中微子末态中的推广。该架构能够原生适配任意中微子多重数下末态中物体类型与多重性的所有组合。在$t\bar{t}$双轻子事件中，该方法对两个中微子的动量及其关联性的重建精度优于最常用的标准解析技术，且能为所有事件提供解。其推理时间显著优于竞争方法，并可通过图形处理单元并行计算进一步缩短。我们将$ν^2$-Flows应用于$t\bar{t}$双轻子事件，结果表明：在解卷分布中，该方法各统计箱的不确定度远更接近完美中微子重建的性能极限。对于所选双微分观测量，$ν^2$-Flows相较中微子加权法将每个统计箱的统计精度提升1.5至2倍，相较椭圆方法最高提升4倍。