The Landscape of Unfolding with Machine Learning
Fuente:
arXiv
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| Autores principales: | , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866917935694479360 |
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| author | Huetsch, Nathan Villadamigo, Javier Mariño Shmakov, Alexander Diefenbacher, Sascha Mikuni, Vinicius Heimel, Theo Fenton, Michael Greif, Kevin Nachman, Benjamin Whiteson, Daniel Butter, Anja Plehn, Tilman |
| author_facet | Huetsch, Nathan Villadamigo, Javier Mariño Shmakov, Alexander Diefenbacher, Sascha Mikuni, Vinicius Heimel, Theo Fenton, Michael Greif, Kevin Nachman, Benjamin Whiteson, Daniel Butter, Anja Plehn, Tilman |
| contents | Recent innovations from machine learning allow for data unfolding, without binning and including correlations across many dimensions. We describe a set of known, upgraded, and new methods for ML-based unfolding. The performance of these approaches are evaluated on the same two datasets. We find that all techniques are capable of accurately reproducing the particle-level spectra across complex observables. Given that these approaches are conceptually diverse, they offer an exciting toolkit for a new class of measurements that can probe the Standard Model with an unprecedented level of detail and may enable sensitivity to new phenomena. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_18807 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | The Landscape of Unfolding with Machine Learning Huetsch, Nathan Villadamigo, Javier Mariño Shmakov, Alexander Diefenbacher, Sascha Mikuni, Vinicius Heimel, Theo Fenton, Michael Greif, Kevin Nachman, Benjamin Whiteson, Daniel Butter, Anja Plehn, Tilman High Energy Physics - Phenomenology Machine Learning High Energy Physics - Experiment Recent innovations from machine learning allow for data unfolding, without binning and including correlations across many dimensions. We describe a set of known, upgraded, and new methods for ML-based unfolding. The performance of these approaches are evaluated on the same two datasets. We find that all techniques are capable of accurately reproducing the particle-level spectra across complex observables. Given that these approaches are conceptually diverse, they offer an exciting toolkit for a new class of measurements that can probe the Standard Model with an unprecedented level of detail and may enable sensitivity to new phenomena. |
| title | The Landscape of Unfolding with Machine Learning |
| topic | High Energy Physics - Phenomenology Machine Learning High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2404.18807 |