论文标题
ν-Flows：有条件的中微子回归
ν-Flows: Conditional Neutrino Regression
论文作者
论文摘要
我们提出了$ν$ - 流，这是一种新的方法，用于限制使用条件归一化流和深层可逆神经网络在高能量对撞机实验中中微子运动学的可能性空间。此方法允许恢复全中微子动量，通常将其作为自由参数保留，并允许一个人在有条件性的可能性观察中进行样本中微子值。我们通过将其应用于模拟半衰减的$ t \ bar {t} $事件，证明了$ν$ - 流的成功，并表明它可以导致更准确的动量重建，尤其是纵向坐标的重建。我们还表明，这在JET关联的下游任务中具有直接的好处，与常规方法相比，提高了1.41倍。
We present $ν$-Flows, a novel method for restricting the likelihood space of neutrino kinematics in high energy collider experiments using conditional normalizing flows and deep invertible neural networks. This method allows the recovery of the full neutrino momentum which is usually left as a free parameter and permits one to sample neutrino values under a learned conditional likelihood given event observations. We demonstrate the success of $ν$-Flows in a case study by applying it to simulated semileptonic $t\bar{t}$ events and show that it can lead to more accurate momentum reconstruction, particularly of the longitudinal coordinate. We also show that this has direct benefits in a downstream task of jet association, leading to an improvement of up to a factor of 1.41 compared to conventional methods.
