SwdFold:A Reweighting and Unfolding method based on Optimal Transport Theory

TL;DR

SwdFold introduces a novel optimal transport-based method for data unfolding in high-energy physics, offering a robust, model-independent approach that improves the accuracy and stability of correcting experimental distortions.

Contribution

The paper presents SwdFold, a new reweighting and unfolding technique leveraging optimal transport theory, enhancing stability and model independence in high-energy physics data analysis.

Findings

Accurately unfolds toy experimental data with simulated distributions.

Maintains physical feature integrity across observables.

Provides a high-precision, model-independent reweighting framework.

Abstract

High-energy physics experiments rely heavily on precise measurements of energy and momentum, yet face significant challenges due to detector limitations, calibration errors, and the intrinsic nature of particle interactions. Traditional unfolding techniques have been employed to correct for these distortions, yet they often suffer from model dependency and stability issues. We present a novel method, SwdFold, which utilizes the principles of optimal transport to provide a robust, model-independent framework to estimate the probability density ratio for data unfolding. It not only unfold the toy experimental event by reweighted simulated data distributions closely with true distributions but also maintains the integrity of physical features across various observables. We can expect it can enable more reliable predictions and comprehensive analyses as a high precision reweighting and unfolding tool in high-energy physics.