Generative unfolding with distribution mapping
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, Schrödinger Bridges and Direct Diffusion, in order to ensure that the models...
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| Hauptverfasser: | , , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
June 2025
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| In: |
SciPost physics
Year: 2025, Jahrgang: 18, Heft: 6, Pages: 1-28 |
| ISSN: | 2542-4653 |
| DOI: | 10.21468/SciPostPhys.18.6.200 |
| Online-Zugang: | Verlag, kostenfrei, Volltext: https://doi.org/10.21468/SciPostPhys.18.6.200 Verlag, kostenfrei, Volltext: https://scipost.org/10.21468/SciPostPhys.18.6.200 
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| Verfasserangaben: | Anja Butter, Sascha Diefenbacher, Nathan Huetsch, Vinicius Mikuni, Benjamin Nachman, Sofia Palacios Schweitzer and Tilman Plehn |
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| 520 | |a 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, Schrödinger Bridges and Direct Diffusion, in order to ensure that the models learn the correct conditional probabilities. This brings distribution mapping (DM) 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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