Phase structure in the baryon density-dependent quark mass model

The properties of phase diagram of strange quark matter in equilibrium with hadronic matter at finite temperature are studied, where the quark phase and hadron phase are treated by baryon density-dependent quark mass model and hadron resonance gas model with hard core repulsion factor, respectively. Our results indicate that the strangeness fraction fs, perturbation parameter C, and confinement parameter D have strong influence on the properties of phase diagram and the formation of strangelets, where a large fs, small C and D favor the formation of strangelets. Consider the isentropic expansion process, we found that the initial entropy per baryon is about 5, which gives a large probability for the formation of strangelets. Furthermore, as the strangeness fraction fs and one gluon-exchange interaction strength C decrease and confinement parameter D increases, the reheating effect becomes more significant, reducing the possibility of forming strangelets. The new phase diagram could support a massive compact star with the maximum mass exceeding twice the solar mass and have a significant impact on the mass-radius relationship for hybrid stars

Quarkyonic matter and quarkyonic stars in an extended RMF model

By combining RMF models and equivparticle models with density-dependent quark masses, we construct explicitly ``a quark Fermi Sea'' and ``a baryonic Fermi surface'' to model the quarkyonic phase, where baryons with momentums ranging from zero to Fermi momentums are included. The properties of nuclear matter, quark matter, and quarkyonic matter are then investigated in a unified manner, where quarkyonic matter is more stable and energy minimization is still applicable to obtain the microscopic properties of dense matter. Three different covariant density functionals TW99, PKDD, and DD-ME2 are adopted in our work, where TW99 gives satisfactory predictions for the properties of nuclear matter both in neutron stars and heavy-ion collisions and quarkyonic transition is unfavorable. Nevertheless, if PKDD with larger slope of symmetry energy $L$ or DD-ME2 with larger skewness coefficient $J$ are adopted, the corresponding EOSs are too stiff according to both experimental and astrophysical constraints. The situation is improved if quarkyonic transition takes place, where the EOSs become softer and can accommodate various experimental and astrophysical constraints

Hybrid Strangeon Stars

It was conjectured that the basic units of the ground state of bulk strong matter may be strange-clusters called strangeons, and they can form self-bound strangeon stars that are highly compact. Strangeon stars can develop a strange quark matter (SQM) core at high densities, particularly in the color-flavor-locking phase, yielding a branch of hybrid strangeon stars. We explore the stellar structure and astrophysical implications of hybrid strangeon stars. We find that hybrid strangeon stars can meet various astrophysical constraints on pulsar masses, radii, and tidal deformabilities. Finally, we show that the strangeon-SQM mixed phase is not preferred if the charge-neutrality condition is imposed at the strangeon-SQM transition region.Comment: 10 pages, 4 figure

CoLFI: Cosmological Likelihood-free Inference with Neural Density Estimators

In previous works, we proposed to estimate cosmological parameters with the artificial neural network (ANN) and the mixture density network (MDN). In this work, we propose an improved method called the mixture neural network (MNN) to achieve parameter estimation by combining ANN and MDN, which can overcome shortcomings of the ANN and MDN methods. Besides, we propose sampling parameters in a hyper-ellipsoid for the generation of the training set, which makes the parameter estimation more efficient. A high-fidelity posterior distribution can be obtained using $\mathcal{O}(10^2)$ forward simulation samples. In addition, we develop a code-named CoLFI for parameter estimation, which incorporates the advantages of MNN, ANN, and MDN, and is suitable for any parameter estimation of complicated models in a wide range of scientific fields. CoLFI provides a more efficient way for parameter estimation, especially for cases where the likelihood function is intractable or cosmological models are complex and resource-consuming. It can learn the conditional probability density $p(\boldsymbol\theta|\boldsymbol{d})$ using samples generated by models, and the posterior distribution $p(\boldsymbol\theta|\boldsymbol{d}_0)$ can be obtained for a given observational data $\boldsymbol{d}_0$. We tested the MNN using power spectra of the cosmic microwave background and Type Ia supernovae and obtained almost the same result as the Markov Chain Monte Carlo method. The numerical difference only exists at the level of $\mathcal{O}(10^{-2}\sigma)$. The method can be extended to higher-dimensional data.Comment: 24 pages, 8 tables, 17 figures, ApJS in press, corrected the ELU plot in Table 5. The code repository is available at https://github.com/Guo-Jian-Wang/colf