Properties of First-Order Hadron-Quark Phase Transition from Inverting Neutron Star Observables

By inverting the observational data of several neutron star observables in the three dimensional parameter space of the constant speed of sound (CSS) model while fixing all hadronic Equation of State parameters at their currently known most probable values, we constrain the three parameters of the CSS model and their correlations. Using two lower radius limits of $R_{2.01}=11.41$ km and $R_{2.01}=12.2$ km for PSR J0740+6620 obtained from two independent analyses using different approaches by the Neutron Star Interior Composition Explorer (NICER) Collaboration, the speed of sound squared $c_{\rm QM}^2$ in quark matter is found to have a lower limit of $0.35$ and $0.43$ in unit of $c^2$, respectively, above its conformal limit of $c_{\rm QM}^2<1/3$. An approximately linear correlation between the first-order hadron-quark transition density $\rho_t$ and its strength $\Delta\varepsilon$ is found. Moreover, the presence of twin star is deemed improbable by the present work.Comment: Added 1 figure and some discussions. Phys. Rev. C in pres

Towards Understanding Astrophysical Effects of Nuclear Symmetry Energy

Determining the Equation of State (EOS) of dense neutron-rich nuclear matter is a shared goal of both nuclear physics and astrophysics. Except possible phase transitions, the density dependence of nuclear symmetry \esym is the most uncertain part of the EOS of neutron-rich nucleonic matter especially at supra-saturation densities. Much progresses have been made in recent years in predicting the symmetry energy and understanding why it is still very uncertain using various microscopic nuclear many-body theories and phenomenological models. Simultaneously, significant progresses have also been made in probing the symmetry energy in both terrestrial nuclear laboratories and astrophysical observatories. In light of the GW170817 event as well as ongoing or planned nuclear experiments and astrophysical observations probing the EOS of dense neutron-rich matter, we review recent progresses and identify new challenges to the best knowledge we have on several selected topics critical for understanding astrophysical effects of the nuclear symmetry energy.Comment: 77 pages. Invited Review Article, EPJA (2019) in pres

Relation Between Gravitational Mass and Baryonic Mass for Non-Rotating and Rapidly Rotating Neutron Stars

With a selected sample of neutron star (NS) equations of state (EOSs) that are consistent with the current observations and have a range of maximum masses, we investigate the relations between NS gravitational mass Mg and baryonic mass Mb, and the relations between the maximum NS mass supported through uniform rotation (Mmax) and that of nonrotating NSs (MTOV). We find that for an EOS-independent quadratic, universal transformation formula (Mb=Mg+A×M2g)(Mb=Mg+A×Mg2), the best-fit A value is 0.080 for non-rotating NSs, 0.064 for maximally rotating NSs, and 0.073 when NSs with arbitrary rotation are considered. The residual error of the transformation is ∼ 0.1M⊙ for non-spin or maximum-spin, but is as large as ∼ 0.2M⊙ for all spins. For different EOSs, we find that the parameter A for non-rotating NSs is proportional to R−11.4R1.4−1 (where R1.4 is NS radius for 1.4M⊙ in units of km). For a particular EOS, if one adopts the best-fit parameters for different spin periods, the residual error of the transformation is smaller, which is of the order of 0.01M⊙ for the quadratic form and less than 0.01M⊙ for the cubic form ((Mb=Mg+A1×M2g+A2×M3g)(Mb=Mg+A1×Mg2+A2×Mg3)). We also find a very tight and general correlation between the normalized mass gain due to spin Δm = (Mmax − MTOV)/MTOV and the spin period normalized to the Keplerian period PP, i.e., log10Δm=(−2.74±0.05)log10P+log10(0.20±0.01)log10Δm=(−2.74±0.05)log10P+log10(0.20±0.01), which is independent of EOS models. These empirical relations are helpful to study NS-NS mergers with a long-lived NS merger product using multi-messenger data. The application of our results to GW170817 is discussed

Improved Hybrid Layered Image Compression using Deep Learning and Traditional Codecs

Recently deep learning-based methods have been applied in image compression and achieved many promising results. In this paper, we propose an improved hybrid layered image compression framework by combining deep learning and the traditional image codecs. At the encoder, we first use a convolutional neural network (CNN) to obtain a compact representation of the input image, which is losslessly encoded by the FLIF codec as the base layer of the bit stream. A coarse reconstruction of the input is obtained by another CNN from the reconstructed compact representation. The residual between the input and the coarse reconstruction is then obtained and encoded by the H.265/HEVC-based BPG codec as the enhancement layer of the bit stream. Experimental results using the Kodak and Tecnick datasets show that the proposed scheme outperforms the state-of-the-art deep learning-based layered coding scheme and traditional codecs including BPG in both PSNR and MS-SSIM metrics across a wide range of bit rates, when the images are coded in the RGB444 domain.Comment: Submitted to Signal Processing: Image Communicatio