Astronuclear
Published:
Convergence properties of the 𝑇′-expansion scheme: Hadron resonance gas and the cluster expansion model
In this study, we assess the effectiveness and robustness of the recently proposed 𝑇′-expansion scheme for expanding the equation of state of strongly interacting matter to finite density, by comparing its performance relative to the conventional Taylor expansion method in various effective QCD models. We use baryon number density and its susceptibilities to calculate the expansion coefficients in the 𝑇′-expansion scheme with and without the Stefan-Boltzmann limit correction. Our methodology involves comparing truncation orders to exact solutions to assess the scheme’s accuracy. We utilize ideal, excluded volume, and van der Waals formulations of the hadron resonance gas (HRG) model at low temperatures, and the cluster expansion model at higher temperatures. Our findings indicate that the 𝑇′-expansion scheme offers superior convergence properties near and above the chiral crossover temperature, where the chiral-criticality-inspired scaling (∂/∂𝑇)𝜇𝐵 ∼(∂2/∂𝜇2 𝐵)𝑇holds. However, it shows limited improvement in the HRG models, indicating that it may not be the most suitable choice for describing the hadronic phase.
Micheal Kahangirwe, Claudia Ratti, Volodymyr Vovchenko, Irene Gonzalez, and Jorge A. Muñoz. Physical Review D 111, 094034 (2025).
Phase Diagram of Nuclear Pastas in Neutron Star Crusts
Two neural networks were trained to predict, respectively, the Euler characteristic and the curvature of nuclear pastas in neutron star crust conditions generated by molecular dynamics simulations of neutron star matter with 0.1 < x < 0.5, 0.040 fm−3 < ρ < 0.085 fm−3 (0.68 × 1014 g/cm3 < ρ < 1.43 × 1014 g/cm3), and 0.2 MeV < T < 4.0 MeV, where x is proton content, the density is 𝜌, and the temperature is T. The predictions of the two networks were combined to determine the nuclear pasta phase that is thermodynamically stable at a given x, 𝜌, and T, and a three-dimensional phase diagram that extrapolated slightly the regions of existing molecular dynamics data was computed. The jungle gym and anti-jungle gym structures are prevalent at high temperature and low density, while the anti-jungle gym and anti-gnocchi structures dominate at high temperature and high density. A diversity of structures exist at low temperatures and intermediate density and proton content. The trained models used in this work are open access and available at a public repository to promote comparison to pastas obtained with other models.
Jorge A. Muñoz and Jorge A. López. Dynamics 4, 157 (2024).
Equilibrium constants of nuclear reactions in supernova explosions
We study the change in internal rotational energy in the transformation of protons to neutrons in the β-decay reactions that take place in the collapse of the iron core of massive stars that precede type II supernova explosions. We consider an ensemble of electrons, protons, neutrons and neutrinos undergoing β-decay reactions, treat the protons and neutrons as triatomic rotors, evaluate the equilibrium constant to obtain the change in rotational energy during the proton-to-neutron transformation. We estimate such change for a variety of conditions, and compare to the energy released in a supernova explosion.
J.A. Muñoz, M.A. Garcia, J.A. Lopez. Int. J. Mod. Phys. E 31, 2250018 (2022)
Machine learning Minkowski functionals of neutron star crusts
We study the structures attained by neutron star matter with proton to neutron ratios ranging from x = 0.05 to 0.5, densities in the range of 0.04 fm^−3 < ρ < 0.08 fm^−3, and temperatures T < 4 MeV. In particular, since the Minkowski functionals are related to the morphology of the the pasta shapes, we study their variation at those densities, temperatures and proton contents using machine learning technology.
J.A. Lopez, J.A. Muñoz, C.O. Dorso, and G.A. Frank. Journal of Physics: Conference Series 1643, 012054 (2020)
Analytical expression and neural network study of the symmetry energy
Motivated by classical molecular dynamics simulations of infinite nuclear sys- tems with varying density, temperature and isospin content, an analytical ex- pression that approximates the symmetry energy at subcritical densities is ob- tained. Similarly a neural network is used to evaluate the symmetry energy in the same temperature-density regime. The resulting expression and neural network can both be used to calculate the symmetry energy at a given density and temperature or, conversely, to extract the temperature of experimental data.
J.A. Lopez and J.A. Muñoz. CERN Proc. 1, 29 (2019)