Unbinned Profiled Unfolding

TL;DR

This paper introduces a novel machine learning-based unbinned unfolding method in particle physics that allows for simultaneous profiling of nuisance parameters, improving the accuracy of differential cross section measurements.

Contribution

The paper presents a new unbinned unfolding technique using machine learning that incorporates profiling of nuisance parameters, addressing limitations of previous methods.

Findings

Successfully demonstrated with Gaussian examples

Applied to simulated Higgs boson cross section measurement

Enables unbinned differential cross section extraction with nuisance profiling

Abstract

Unfolding is an important procedure in particle physics experiments which corrects for detector effects and provides differential cross section measurements that can be used for a number of downstream tasks, such as extracting fundamental physics parameters. Traditionally, unfolding is done by discretizing the target phase space into a finite number of bins and is limited in the number of unfolded variables. Recently, there have been a number of proposals to perform unbinned unfolding with machine learning. However, none of these methods (like most unfolding methods) allow for simultaneously constraining (profiling) nuisance parameters. We propose a new machine learning-based unfolding method that results in an unbinned differential cross section and can profile nuisance parameters. The machine learning loss function is the full likelihood function, based on binned inputs at detector-level. We first demonstrate the method with simple Gaussian examples and then show the impact on a simulated Higgs boson cross section measurement.