Table of Links
-
Unfolding
-
Results
Appendices
A. Conditional DDPM Loss Derivation
C. Detector Simulation and Jet Matching
5 Results
5.1 Toy models
Proof-of-concept was demonstrated using toy models with non-physics data. To evaluate the unfolding performance, we calculated the 1-dimensional Wasserstein and Energy distances between the truth-level, unfolded, and detector-level data for each component in the data vectors of the samples. We also computed the Wasserstein distance and KL divergence between the histograms of the truth-level data and those of the
unfolded and detector-level data. The sample-based Wasserstein distances are displayed on each plot, and a comprehensive list of the metrics is provided in appendix D.
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
