Early-times Yang-Mills dynamics and the characterization of strongly interacting matter with statistical learning
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| _version_ | 1866910496201900032 |
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| author | Heffernan, Matthew R. Gale, Charles Jeon, Sangyong Paquet, Jean-François |
| author_facet | Heffernan, Matthew R. Gale, Charles Jeon, Sangyong Paquet, Jean-François |
| contents | In ultrarelativistic heavy-ion collisions, a plasma of deconfined quarks and gluons is formed within $1$ fm/c of the nuclei's impact. The complex dynamics of the collision before $\approx 1$ fm/c is often described with parametric models, which affect the predictivity of calculations. In this work, we perform a systematic analysis of LHC measurements from Pb-Pb collisions, by combining an \emph{ab-initio} model of the early stage of the collisions with a hydrodynamic model of the plasma. We obtain state-of-the-art constraints on the shear and bulk viscosity of quark-gluon plasma. We mitigate the additional cost of the ab-initio initial conditions by combining Bayesian model averaging with transfer learning, allowing us to account for important theoretical uncertainties in the hydrodynamics-to-hadron transition. We show that, despite the apparent strong constraints on the shear viscosity, metrics that balance the model's predictivity with its degree of agreement with data do not prefer a temperature-dependent specific shear viscosity over a constant value. We validate the model by comparing with discriminating observables not used in the calibration, finding excellent agreement. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_09619 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Early-times Yang-Mills dynamics and the characterization of strongly interacting matter with statistical learning Heffernan, Matthew R. Gale, Charles Jeon, Sangyong Paquet, Jean-François Nuclear Theory High Energy Physics - Phenomenology Nuclear Experiment In ultrarelativistic heavy-ion collisions, a plasma of deconfined quarks and gluons is formed within $1$ fm/c of the nuclei's impact. The complex dynamics of the collision before $\approx 1$ fm/c is often described with parametric models, which affect the predictivity of calculations. In this work, we perform a systematic analysis of LHC measurements from Pb-Pb collisions, by combining an \emph{ab-initio} model of the early stage of the collisions with a hydrodynamic model of the plasma. We obtain state-of-the-art constraints on the shear and bulk viscosity of quark-gluon plasma. We mitigate the additional cost of the ab-initio initial conditions by combining Bayesian model averaging with transfer learning, allowing us to account for important theoretical uncertainties in the hydrodynamics-to-hadron transition. We show that, despite the apparent strong constraints on the shear viscosity, metrics that balance the model's predictivity with its degree of agreement with data do not prefer a temperature-dependent specific shear viscosity over a constant value. We validate the model by comparing with discriminating observables not used in the calibration, finding excellent agreement. |
| title | Early-times Yang-Mills dynamics and the characterization of strongly interacting matter with statistical learning |
| topic | Nuclear Theory High Energy Physics - Phenomenology Nuclear Experiment |
| url | https://arxiv.org/abs/2306.09619 |