Refine
Document Type
- Article (2)
Language
- English (2) (remove)
Has Fulltext
- yes (2)
Is part of the Bibliography
- no (2) (remove)
Institute
- Physik (2) (remove)
We introduce a novel technique that utilizes a physics-driven deep learning method to reconstruct the dense matter equation of state from neutron star observables, particularly the masses and radii. The proposed framework involves two neural networks: one to optimize the EoS using Automatic Differentiation in the unsupervised learning scheme; and a pre-trained network to solve the Tolman–Oppenheimer–Volkoff (TOV) equations. The gradient-based optimization process incorporates a Bayesian picture into the proposed framework. The reconstructed EoS is proven to be consistent with the results from conventional methods. Furthermore, the resulting tidal deformation is in agreement with the limits obtained from the gravitational wave event, GW170817.
We investigate viscous effects on the dynamical evolution of QCD matter during the first-order phase transition, which may happen in heavy-ion collisions. We first obtain the first-order phase transition line in the QCD phase diagram under the Gibbs condition by using the MIT bag model and the hadron resonance gas model for the equation of state of partons and hadrons. The viscous pressure, which corresponds to the friction in the energy balance, is then derived from the energy and net baryon number conservation during the phase transition. We find that the viscous pressure relates to the thermodynamic change of the two-phase state and thus affects the timescale of the phase transition. Numerical results are presented for demonstrations.