Machine Learning-based Unfolding for Cross Section Measurements in the Presence of Nuisance Parameters

TL;DR

This paper extends machine learning-based unfolding methods to account for nuisance parameters, improving the correction of measured cross sections in particle physics experiments with complex detector effects.

Contribution

We introduce Profile OmniFold, an extension of OmniFold that incorporates nuisance parameters into the unfolding process, enhancing its robustness and applicability.

Findings

Successfully demonstrated on Gaussian example

Applied to CMS simulated data from LHC

Improved handling of model uncertainties

Abstract

Statistically correcting measured cross sections for detector effects is an important step across many applications. In particle physics, this inverse problem is known as unfolding. In cases with complex instruments, the distortions they introduce are often known only implicitly through simulations of the detector. Modern machine learning has enabled efficient simulation-based approaches for unfolding high-dimensional data. Among these, one of the first methods successfully deployed on experimental data is the OmniFold algorithm, a classifier-based Expectation-Maximization procedure. In practice, however, the forward model is only approximately specified, and the corresponding uncertainty is encoded through nuisance parameters. Building on the well-studied OmniFold algorithm, we show how to extend machine learning-based unfolding to incorporate nuisance parameters. Our new algorithm, called Profile OmniFold, is demonstrated using a Gaussian example as well as a particle physics case study using simulated data from the CMS Experiment at the Large Hadron Collider.