TY  - JOUR
PB  - SciPost Foundation
DO  - 10.21468/SciPostPhysCore.8.4.064
TI  - Towards universal unfolding of detector effects in high-energy physics using denoising diffusion probabilistic models
PY  - 2025/10/02
UR  - https://www.scipost.org/SciPostPhysCore.8.4.064
JF  - SciPost Physics Core
JA  - SciPost Phys. Core
VL  - 8
IS  - 4
SP  - 064
A1  - Pazos, Camila
AU  - Aeron, Shuchin
AU  - Beauchemin, Pierre-Hugues
AU  - Croft, Vincent
AU  - Huan, Zhengyan
AU  - Klassen, Martin
AU  - Wongjirad, Taritree
AB  - 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.
ER  -

@Article{10.21468/SciPostPhysCore.8.4.064,
	title={{Towards universal unfolding of detector effects in high-energy physics using denoising diffusion probabilistic models}},
	author={Camila Pazos and Shuchin Aeron and Pierre-Hugues Beauchemin and Vincent Croft and Zhengyan Huan and Martin Klassen and Taritree Wongjirad},
	journal={SciPost Phys. Core},
	volume={8},
	pages={064},
	year={2025},
	publisher={SciPost},
	doi={10.21468/SciPostPhysCore.8.4.064},
	url={https://scipost.org/10.21468/SciPostPhysCore.8.4.064},
}