标题： 频率学派关于神经密度比的不确定性以及wifi集合 显示英文标题

标题： Frequentist Uncertainties on Neural Density Ratios with wifi Ensembles

摘要： 我们引入了wifi集合作为一个新颖的框架，用于获得密度比的渐近频率不确定性，特别关注高能物理中的神经比率估计。当感兴趣的密度比是基于参数的似然比时，wifi集合可用于对这些参数进行基于模拟的推断。训练基函数f_i(x)后，可以简单地将权重w_i的不确定性传播到估计的参数上，而无需额外的自助法。为了展示这种方法，我们展示了在大型强子对撞机上的量子色动力学的一个应用，使用wifi集合来估计生成的夸克和胶子喷流之间的似然比。我们使用这个学习到的似然比来估计合成混合夸克/胶子样本中的夸克分数，显示结果的不确定性在经验上满足所需的覆盖属性。 显示英文摘要

摘要： We introduce wifi ensembles as a novel framework to obtain asymptotic frequentist uncertainties on density ratios, with a particular focus on neural ratio estimation in the context of high-energy physics. When the density ratio of interest is a likelihood ratio conditioned on parameters, wifi ensembles can be used to perform simulation-based inference on those parameters. After training the basis functions f_i(x), uncertainties on the weights w_i can be straightforwardly propagated to the estimated parameters without requiring extraneous bootstraps. To demonstrate this approach, we present an application in quantum chromodynamics at the Large Hadron Collider, using wifi ensembles to estimate the likelihood ratio between generated quark and gluon jets. We use this learned likelihood ratio to estimate the quark fraction in a synthetic mixed quark/gluon sample, showing that the resultant uncertainties empirically satisfy the desired coverage properties.