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Learning Uncertainties the Frequentist Way: Calibration and Correlation in High Energy Physics
Rikab Gambhir · Jesse Thaler · Benjamin Nachman

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in Machine Learning and the Physical Sciences »

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 GaussianAnsatz---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 upward of 15.

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Author Information

Rikab Gambhir (MIT)
Jesse Thaler (MIT)
Benjamin Nachman (Lawrence Berkeley National Laboratory)

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