Table of Links
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Unfolding
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Results
Appendices
A. Conditional DDPM Loss Derivation
C. Detector Simulation and Jet Matching
E Complete Physics Results
In fig. 11 and fig. 12 we present the unfolding results for the remaining particle properties (η, ϕ, E, px, py, pz) that were not shown in the main text. These additional plots demonstrate the cDDPM’s ability to successfully unfold the full particle vector, providing a comprehensive view of its performance across all dimensions.
The tables in this appendix (table 3 and table 4) provide a detailed breakdown of the unfolding performance metrics for each particle property and dataset. Furthermore, these metrics (Energy distance, Wasserstein distance between histograms, and KL divergence between histograms) offer a more in-depth perspective on the unfolding quality.
Authors:
(1) Camila Pazos, Department of Physics and Astronomy, Tufts University, Medford, Massachusetts;
(2) Shuchin Aeron, Department of Electrical and Computer Engineering, Tufts University, Medford, Massachusetts and The NSF AI Institute for Artificial Intelligence and Fundamental Interactions;
(3) Pierre-Hugues Beauchemin, Department of Physics and Astronomy, Tufts University, Medford, Massachusetts and The NSF AI Institute for Artificial Intelligence and Fundamental Interactions;
(4) Vincent Croft, Leiden Institute for Advanced Computer Science LIACS, Leiden University, The Netherlands;
(5) Martin Klassen, Department of Physics and Astronomy, Tufts University, Medford, Massachusetts;
(6) Taritree Wongjirad, Department of Physics and Astronomy, Tufts University, Medford, Massachusetts and The NSF AI Institute for Artificial Intelligence and Fundamental Interactions.
This paper is
