A Bayesian framework with analytical TOV linear-response gradients and a neural-network equation of state reconstructs neutron star EoSs and constrains first-order phase transition parameters from simulated mass-radius data.
A deep neural network approach to solve the Dirac equation
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abstract
We extend the method from [Naito, Naito, and Hashimoto, Phys. Rev. Research 5, 033189 (2023)] to solve the Dirac equation not only for the ground state but also for low-lying excited states using a deep neural network and the unsupervised machine learning technique. The variational method fails because of the Dirac sea, which is avoided by introducing the inverse Hamiltonian method. For low-lying excited states, two methods are proposed, which have different performances and advantages. The validity of this method is verified by the calculations with the Coulomb and Woods-Saxon potentials.
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Towards constraining QCD phase transitions in neutron star interiors: Bayesian Inference with TOV linear response analysis
A Bayesian framework with analytical TOV linear-response gradients and a neural-network equation of state reconstructs neutron star EoSs and constrains first-order phase transition parameters from simulated mass-radius data.