Jul 6 – 8, 2021
Europe/Zurich timezone

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


Rikab Gambhir (MIT)


A common problem that appears in collider physics is the inference of a random variable $Y$ given a measurement of another random variable $X$, and the estimation of the uncertainty on $Y$. Additionally, one would like to quantify the extent to which $X$ and $Y$ are related. We present a machine learning framework for performing frequentist maximum likelihood inference with uncertainty estimation and measuring the mutual information between random variables. By using the Donsker-Varadhan representation of the KL divergence, the framework learns the likelihood ratio $p(x|y)/p(x)$. This can be used to calculate the mutual information between $X$ and $Y$. The framework is parameterized using a Gaussian ansatz, which enables a manifest extraction of the maximum likelihood values and uncertainties. All of this can be accomplished in a single training of the model. We then demonstrate our framework for a simple Gaussian example, apply it to a realistic calibration task by calculating jet energy correction (JEC) and jet energy resolution (JER) factors for CMS open data.

Affiliation Massachusetts Institute of Technology
Academic Rank PhD Student

Primary author


Jesse Thaler (MIT) Ben Nachman (Lawrence Berkeley National Lab. (US))

Presentation materials