We propose the NFLikelihood, an unsupervised version, based on Normalizing
Flows, of the DNNLikelihood proposed in Ref.[1]. We show, through realistic
examples, how Autoregressive Flows, based on affine and rational quadratic
spline bijectors, are able to learn complicated high-dimensional Likelihoods
arising in High Energy Physics (HEP) analyses. We focus on a toy LHC analysis
example already considered in the literature and on two Effective Field Theory
fits of flavor and electroweak observables, whose samples have been obtained
throught the HEPFit code. We discuss advantages and disadvantages of the
unsupervised approach with respect to the supervised one and discuss possible
interplays of the two.

我们提出了 NFLikelihood，这是一种基于 Normalizing Flows 的无监督版本的 DNNLikelihood，通过现实例子展示了基于仿射和有理二次样条双射的自回归流如何学习出现在高能物理分析中的复杂高维似然。我们聚焦于一个已经存在于文献中的玩具 LHC 分析例子以及两个有效场论拟合的味道和电弱观测量，这些样本是通过 HEPFit 代码获得的。我们讨论了无监督方法相对于有监督方法的优缺点，并讨论了两者之间的可能相互作用。