Moment Unfolding

TL;DR

This paper introduces a machine learning-based method called Moment Unfolding that directly recovers distribution moments from collider data without binning, improving precision over traditional techniques.

Contribution

It presents a novel moment unfolding approach inspired by GANs that bypasses histogram binning, enabling more accurate analysis of collider physics data.

Findings

More precise than bin-based unfolding methods

Comparable or superior to unbinned approaches

Effective in jet substructure measurements

Abstract

Deconvolving ("unfolding'') detector distortions is a critical step in the comparison of cross section measurements with theoretical predictions in particle and nuclear physics. However, most existing approaches require histogram binning while many theoretical predictions are at the level of statistical moments. We develop a new approach to directly unfold distribution moments as a function of another observable without having to first discretize the data. Our Moment Unfolding technique uses machine learning and is inspired by Generative Adversarial Networks (GANs). We demonstrate the performance of this approach using jet substructure measurements in collider physics. With this illustrative example, we find that our Moment Unfolding protocol is more precise than bin-based approaches and is as or more precise than completely unbinned methods.