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：摘要：本文提出 $\nu^2$-Flows 方法，这是对 $\nu$-Flows 方法的扩展，适用于含多个中微子的末态。该架构能原生适配任意中微子数目下末态中物体类型与多重性的所有组合。在 $t\bar{t}$ 双轻子事例中，双中微子动量及其之间的关联重建精度优于最常用的标准解析技术，且所有事例均能获得解。推理速度显著快于竞争方法，通过图形处理单元并行计算可进一步缩短时间。我们将 $\nu^2$-Flows 应用于 $t\bar{t}$ 双轻子事例，发现反卷积分布中每个 bin 的不确定度远低于标准技术，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，相较于中微子权重法，$\nu^2$-Flows 使每个 bin 的统计精度提升 1.5 至 2 倍；相较于椭圆方法，提升幅度最高可达 4 倍。