  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 as the statistical significance of the
signal-plus-background hypothesis over the background-only one. We present here
a simple method that combines the power of current machine-learning techniques
to face high-dimensional data with the likelihood-based inference tests used in
traditional analyses, which allows us to estimate the sensitivity for both
discovery and exclusion limits through a single parameter of interest, the
signal strength. Based on supervised learning techniques, it can perform well
also with high-dimensional data, when traditional techniques cannot. We apply
the method to a toy model first, so we can explore its potential, and then to a
LHC study of new physics particles in dijet final states. Considering as the
optimal statistical significance the one we would obtain if the true generative
functions were known, we show that our method provides a better approximation
than the usual naive counting experimental results.
