Commission B3 Astroinformatics and Astrostatistics
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Next IAU-IAA Astrostats & Astroinfo online seminar: 09 Apr 2024 / 08:00 (UTC)
Ming-Zhe Han (Purple Mountain Observatory, China)
Parametrized Neutron Star Equation of State with Neural Networks
Abstract: Neutron star equation of state (EoS) is the key to study the properties of cold dense matter. However, the first principle calculations of the EoS have very large uncertainties and are model dependent. Therefore, to use the multi-messenger data of NS to constrain the EoS, people usually use phenomenological models to describe NS EoSs. Parametric models with specific function form are easy to handle, while they are hard to describe some special EoSs. I will introduce a so called nonparametric EoS model based on the feed forward neural network, which can cover more EoS parameter space than the parametric ones. Then I will introduce how to use these models to constrain the NS EoS given the multi-messenger data of NS under the Bayesian framework.
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