A method for approximating optimal statistical significances with machine-learned likelihoods

A method for approximating optimal statistical significances with machine-learned likelihoods

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Abstract Machine-learning techniques have become fundamental in billionaire boys club discount code 2023 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 igora vibrance 6-12 known, we show that our method provides a better approximation than the usual naive counting experimental results.