Generative Unfolding with Distribution Mapping
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866913910474407936 |
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| author | Butter, Anja Diefenbacher, Sascha Huetsch, Nathan Mikuni, Vinicius Nachman, Benjamin Schweitzer, Sofia Palacios Plehn, Tilman |
| author_facet | Butter, Anja Diefenbacher, Sascha Huetsch, Nathan Mikuni, Vinicius Nachman, Benjamin Schweitzer, Sofia Palacios Plehn, Tilman |
| contents | 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 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_02495 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Generative Unfolding with Distribution Mapping Butter, Anja Diefenbacher, Sascha Huetsch, Nathan Mikuni, Vinicius Nachman, Benjamin Schweitzer, Sofia Palacios Plehn, Tilman High Energy Physics - Phenomenology Machine Learning High Energy Physics - Experiment 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 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. |
| title | Generative Unfolding with Distribution Mapping |
| topic | High Energy Physics - Phenomenology Machine Learning High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2411.02495 |