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Structure of Quark Star: A Comparative Analysis of Bayesian Inference and Neural Network Based Modeling

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arxiv 2007.10239 v2 pith:L4FCAEMZ submitted 2020-07-20 nucl-th astro-ph.HE

Silvia Traversi , Prasanta Char This is my paper
classification nucl-thastro-ph.HE
keywords quarkbayesianinferencemassmethodsnetworkneuralsound
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In this work, we compare two powerful parameter estimation methods namely Bayesian inference and Neural Network based learning to study the quark matter equation of state with constant speed of sound parametrization and the structure of the quark stars within the two-family scenario. We use the mass and radius estimations from several X-ray sources and also the mass and tidal deformability measurements from gravitational wave events to constrain the parameters of our model. The results found from the two methods are consistent. The predicted speed of sound is compatible with the conformal limit.

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Cited by 1 Pith paper

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  1. Topological Uncertainty for Anomaly Detection in the Neural-network EoS Inference with Neutron Star Data

    nucl-th 2025-08 conditional novelty 4.0 of 10

    Applying Topological Uncertainty to hidden-layer activations of a trained FNN detects failed neutron-star EoS inferences with over 90% success in the best-tested configuration.

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