The Landscape of Unfolding with Machine Learning

TL;DR

This paper reviews and evaluates machine learning methods for data unfolding in particle physics, demonstrating their accuracy and potential to enhance Standard Model measurements and discover new phenomena.

Contribution

It introduces and compares multiple ML-based unfolding techniques, highlighting their effectiveness and diversity for complex, high-dimensional data analysis.

Findings

All methods accurately reproduce particle-level spectra.

ML approaches offer detailed, multidimensional insights.

Potential to improve sensitivity to new physics phenomena.

Abstract

Recent innovations from machine learning allow for data unfolding, without binning and including correlations across many dimensions. We describe a set of known, upgraded, and new methods for ML-based unfolding. The performance of these approaches are evaluated on the same two datasets. We find that all techniques are capable of accurately reproducing the particle-level spectra across complex observables. Given that these approaches are conceptually diverse, they offer an exciting toolkit for a new class of measurements that can probe the Standard Model with an unprecedented level of detail and may enable sensitivity to new phenomena.