A Practical Guide to Unbinned Unfolding

TL;DR

This paper provides a practical guide to unbinned unfolding techniques in high-energy physics, highlighting recent machine learning approaches that improve data analysis flexibility and accuracy.

Contribution

It offers practical recommendations and insights from researchers applying unbinned unfolding methods to real experimental data, advancing current data correction practices.

Findings

Unbinned unfolding enables higher-dimensional data analysis.

Machine learning techniques improve unfolding accuracy.

Guidelines facilitate adoption of unbinned methods.

Abstract

Unfolding, in the context of high-energy particle physics, refers to the process of removing detector distortions in experimental data. The resulting unfolded measurements are straightforward to use for direct comparisons between experiments and a wide variety of theoretical predictions. For decades, popular unfolding strategies were designed to operate on data formatted as one or more binned histograms. In recent years, new strategies have emerged that use machine learning to unfold datasets in an unbinned manner, allowing for higher-dimensional analyses and more flexibility for current and future users of the unfolded data. This guide comprises recommendations and practical considerations from researchers across a number of major particle physics experiments who have recently put these techniques into practice on real data.