Towards a data-driven model of hadronization using normalizing flows
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866929456676864000 |
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| author | Bierlich, Christian Ilten, Phil Menzo, Tony Mrenna, Stephen Szewc, Manuel Wilkinson, Michael K. Youssef, Ahmed Zupan, Jure |
| author_facet | Bierlich, Christian Ilten, Phil Menzo, Tony Mrenna, Stephen Szewc, Manuel Wilkinson, Michael K. Youssef, Ahmed Zupan, Jure |
| contents | We introduce a model of hadronization based on invertible neural networks that faithfully reproduces a simplified version of the Lund string model for meson hadronization. Additionally, we introduce a new training method for normalizing flows, termed MAGIC, that improves the agreement between simulated and experimental distributions of high-level (macroscopic) observables by adjusting single-emission (microscopic) dynamics. Our results constitute an important step toward realizing a machine-learning based model of hadronization that utilizes experimental data during training. Finally, we demonstrate how a Bayesian extension to this normalizing-flow architecture can be used to provide analysis of statistical and modeling uncertainties on the generated observable distributions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_09296 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Towards a data-driven model of hadronization using normalizing flows Bierlich, Christian Ilten, Phil Menzo, Tony Mrenna, Stephen Szewc, Manuel Wilkinson, Michael K. Youssef, Ahmed Zupan, Jure High Energy Physics - Phenomenology High Energy Physics - Experiment We introduce a model of hadronization based on invertible neural networks that faithfully reproduces a simplified version of the Lund string model for meson hadronization. Additionally, we introduce a new training method for normalizing flows, termed MAGIC, that improves the agreement between simulated and experimental distributions of high-level (macroscopic) observables by adjusting single-emission (microscopic) dynamics. Our results constitute an important step toward realizing a machine-learning based model of hadronization that utilizes experimental data during training. Finally, we demonstrate how a Bayesian extension to this normalizing-flow architecture can be used to provide analysis of statistical and modeling uncertainties on the generated observable distributions. |
| title | Towards a data-driven model of hadronization using normalizing flows |
| topic | High Energy Physics - Phenomenology High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2311.09296 |