A method for approximating optimal statistical significances with machine-learned likelihoods
Abstract Machine-learning techniques have become fundamental in high-energy physics and, for new physics searches, it is crucial to know their performance in terms of experimental sensitivity, understood omnia 943 lever as the statistical significance of the signal-plus-background hypothesis over the background-only one.We present here a simple met