Generative Unfolding with Distribution Mapping

• URL: http://arxiv.org/abs/2411.02495v1
• Date: Mon, 04 Nov 2024 19:00:01 GMT
• Title: Generative Unfolding with Distribution Mapping
• Authors: Anja Butter, Sascha Diefenbacher, Nathan Huetsch, Vinicius Mikuni, Benjamin Nachman, Sofia Palacios Schweitzer, Tilman Plehn,
• Abstract summary: We show how to extend two morphing techniques, Schr"odinger Bridges and Direct Diffusion, in order to ensure that the models learn the correct conditional probabilities. Results are presented with a standard benchmark dataset of single jet substructure as well as for a new dataset describing a 22-dimensional phase space of Z + 2-jets.
• Score: 0.0837622912636323
• License:
• Abstract: Machine learning enables unbinned, highly-differential cross section measurements. A recent idea uses generative models to morph a starting simulation into the unfolded data. We show how to extend two morphing techniques, Schr\"odinger Bridges and Direct Diffusion, in order to ensure that the models learn the correct conditional probabilities. This brings distribution mapping to a similar level of accuracy as the state-of-the-art conditional generative unfolding methods. Numerical results are presented with a standard benchmark dataset of single jet substructure as well as for a new dataset describing a 22-dimensional phase space of Z + 2-jets.

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