Learning Uncertainties the Frequentist Way: Calibration and Correlation in High Energy Physics

Abstract

Calibration is a common experimental physics problem, whose goal is to infer the value and uncertainty of an unobservable quantity Z given a measured quantity X. Additionally, one would like to quantify the extent to which X and Z are correlated. In this paper, we present a machine learning framework for performing frequentist maximum likelihood inference with Gaussian uncertainty estimation, which also quantifies the mutual information between the unobservable and measured quantities. This framework uses the Donsker-Varadhan representation of the Kullback-Leibler divergence -- parametrized with a novel Gaussian Ansatz -- to enable a simultaneous extraction of the maximum likelihood values, uncertainties, and mutual information in a single training. We demonstrate our framework by extracting jet energy corrections and resolution factors from a simulation of the CMS detector at the Large Hadron Collider. By leveraging the high-dimensional feature space inside jets, we improve upon the nominal CMS jet resolution by upwards of 15%.

7 pages, 1 figure, 1 table, code available at https://github.com/rikab/GaussianAnsatz; v3: minor updates to match journal version; v4: reference updates

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Cited by in corpus (3)

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• Bias and Priors in Machine Learning Calibrations for High Energy Physics
• Accurate Surrogate Amplitudes with Calibrated Uncertainties