Towards Universal Unfolding of Detector Effects in High-Energy Physics using Denoising Diffusion Probabilistic Models

TL;DR

This paper presents a novel, flexible, and scalable unfolding method for high-energy physics data using conditional Denoising Diffusion Probabilistic Models, aiming to reduce dependence on simulations and improve generalization to unseen processes.

Contribution

The paper introduces a non-iterative, diffusion-based unfolding approach that incorporates distribution moments for conditioning, enabling universal and adaptable detector effect correction.

Findings

Demonstrates improved accuracy over traditional methods

Shows strong generalization to unseen physics processes

Reduces reliance on explicit distribution assumptions

Abstract

Correcting for detector effects in experimental data, particularly through unfolding, is critical for enabling precision measurements in high-energy physics. However, traditional unfolding methods face challenges in scalability, flexibility, and dependence on simulations. We introduce a novel approach to multidimensional object-wise unfolding using conditional Denoising Diffusion Probabilistic Models (cDDPM). Our method utilizes the cDDPM for a non-iterative, flexible posterior sampling approach, incorporating distribution moments as conditioning information, which exhibits a strong inductive bias that allows it to generalize to unseen physics processes without explicitly assuming the underlying distribution. Our results highlight the potential of this method as a step towards a "universal" unfolding tool that reduces dependence on truth-level assumptions, while enabling the unfolding of a wide range of measured distributions with improved adaptability and accuracy.