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arXiv · 2510.19906

Generative Unfolding of Jets and Their Substructure

Antoine Petitjean·Anja Butter·Kevin Greif·Sofia Palacios Schweitzer·Tilman Plehn·Jonas Spinner·Daniel Whiteson
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Abstract

Unfolding, for example of distortions imparted by detectors, provides suitable and publishable representations of LHC data. Many methods for unbinned and high-dimensional unfolding using machine learning have been proposed, but no generative method scales to the several hundred dimensions necessary to fully characterize LHC collisions. This paper proposes a 3-stage generative unfolding framework that is capable of unfolding several hundred dimensions. It is effective to unfold the jet-level kinematics as well as the full substructure of light-flavor jets and of top jets, and is the first generative unfolding study to achieve high precision on high-dimensional jet substructure.

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Antoine Petitjean, Anja Butter, Kevin Greif, Sofia Palacios Schweitzer, Tilman Plehn, Jonas Spinner, Daniel Whiteson. 2025-10-22. Generative Unfolding of Jets and Their Substructure. https://arxiv.org/abs/2510.19906

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