Speaker
Description
Unfolding detector distortions in experimental data 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 universal unfolding approach using conditional denoising diffusion probabilistic models (cDDPM). By modeling only the conditional probability density between detector-level observations and truth-level particle properties, 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. This method for universal unfolding could impact measurements of the Standard Model and searches for new physics by reducing dependence on error-prone, analysis-specific corrections of detector effects at the LHC and future colliders.
