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.

翻译：我们提出NFLikelihood——一种基于归一化流的无监督版本，该版本源自参考文献[1]中提出的DNNLikelihood方法。通过实际案例，我们展示了基于仿射和有理二次样条双射器的自回归流如何能够学习高能物理（HEP）分析中出现的复杂高维似然函数。我们重点分析了文献中已探讨的LHC案例分析示例，以及通过HEPFit代码获得的味观测量与电弱观测量两个有效场论拟合案例。我们讨论了无监督方法相较于有监督方法的优缺点，并探讨了两种方法可能的互操作方式。