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
3.1 Conditional DDPM
Conditioning methods for DDPMs can either use conditions to guide unconditional DDPMs in the reverse process [7], or they can incorporate direct conditions to the learned reverse process. While guided diffusion methods have had great success in image synthesis [10], direct conditioning provides a framework that is particularly useful in unfolding.
We implement a conditional DDPM (cDDPM) for unfolding that keeps the original unconditional forward process and introduces a simple, direct conditioning on y to the reverse process,
This conditioned reverse process learns to directly estimate the posterior probability P(x|y) through its Gaussian transitions. More specifically, the reverse process, parameterized by θ, learns to remove the introduced noise to recover the target value x by conditioning directly on y
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
