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
2 Unfolding
2.1 Posing the Unfolding Problem
This reveals one of the main challenges in developing a universal unfolder, which can be applied to unfold detector data for any physics process. Instead of developing a method able to learn a posterior P(x|y) to unfold detector data pertaining to a specific true underlying distribution, a universal unfolder aims to remove detector effects from any set of measured data agnostic of the process of interest, ideally with no bias towards any prior distribution.
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
