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

Download paper here

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.

Download paper here

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.

Download paper here

J.A. Lopez and J.A. Muñoz. CERN Proc. 1, 29 (2019)