Jun 10 – 13, 2024
Europe/Paris timezone

Unfolding using Denoising Diffusion (20+20)

Jun 13, 2024, 10:10 AM


Camila Pazos (Tufts University (US))


Unfolding detector distortions in experimental data is critical for enabling precision measurements in high-energy physics (HEP). However, traditional unfolding methods face challenges in scalability, flexibility, and dependence on simulations. We introduce a novel unfolding approach using conditional denoising diffusion probabilistic models (cDDPM). By modeling the conditional probability density between detector-level observations and truth-level particle properties from various physics processes, the cDDPM unfolding performance generalizes across varied simulated processes and kinematic distributions without retraining. We demonstrate proof-of-concept on toy models and evaluate on simulated Large Hadron Collider jets across different physics processes.

Primary author

Camila Pazos (Tufts University (US))


Martin Klassen (Tufts University (US)) Pierre-Hugues Beauchemin (Tufts University (US)) Prof. Shuchin Aeron (Tufts University) Taritree Wongjirad (Tufts University) Vincent Alexander Croft (Nikhef National institute for subatomic physics (NL))

Presentation materials