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The recent detection of gravitational waves from merging neutron star events has opened a new window on the many unknown aspects of their internal dynamics. A key role in this context is played by the transition from baryon to quark matter…
For densities beyond nuclear saturation, there is still a large uncertainty in the equations of state (EoS) of dense matter that translate into uncertainties in the internal structure of neutron stars. The MUSES Calculation Engine provides…
The profile of a neutron star probes a very large range of densities, from the density of iron up to several times the density of saturated nuclear matter, and thus no theory of hadrons can be considered reliable if extended to those…
In this paper, we examine neutron star structure in perturbative $f(R)$ gravity models with realistic equation of state. We obtain mass-radius relations in two gravity models of the form $f_{1}(R)=R+ \alpha R(e^{-R/R_0}-1)$ and…
Due to its powerful capability and high efficiency in big data analysis, machine learning has been applied in various fields. We construct a neural network platform to constrain the behaviors of the equation of state of nuclear matter with…
Ab initio methods using weakly interacting nucleons give a good description of condensed nuclear matter up to densities comparable to the nuclear saturation density. At higher densities palpable strong interactions between overlapping…
We study neutron star configurations in a simple shift-symmetric subfamily of degenerate higher-order scalar-tensor (DHOST) theories, whose deviations from General Relativity (GR) are characterized by a single parameter. We compute the…
We perform a systematic analysis of neutron star radius constraints from five quiescent low-mass X-ray binaries and examine how they depend on measurements of their distances and amounts of intervening absorbing material, as well as their…
In this contribution, we briefly present the equation-of-state modelling for application to neutron stars and discuss current constraints coming from nuclear physics theory and experiments. To assess the impact of model uncertainties, we…
Neural networks (NNs) are inherently multidimensional classifiers that learn complex, non-linear relationships among input observables. While their flexibility enables unprecedented performance in high-energy physics (HEP) analyses, it also…
Neutron stars can be regarded as natural laboratories that enable us to investigate nuclear matter properties under extreme conditions that are otherwise impossible to access in terrestrial experiments. Astrophysical observations of neutron…
We study oscillations and instabilities of relativistic stars using perturbation theory in general relativity and take into account the contribution of a dynamic spacetime. We present the oscillation spectrum as well as the critical values…
We present a neural network classification model for detecting the presence of hyperonic degrees of freedom in neutron stars. The models take radii and/or tidal deformabilities as input and give the probability for the presence of hyperons…
Equilibria of binary neutron stars in close circular orbits are computed numerically in a waveless formulation: The full Einstein-relativistic-Euler system is solved on an initial hypersurface to obtain an asymptotically flat form of the…
This dissertation focuses on the reconstruction of Equations of State (EoSs) describing the interior of compact stars, using modern machine learning and deep learning methods. The pipeline is based on data from mass-radius (M-R) curves,…
Characterizing the fundamental parameters of stars from observations is crucial for studying the stars themselves, their planets, and the galaxy as a whole. Stellar evolution theory predicting the properties of stars as a function of…
Using an explicitly isospin-dependent parametric Equation of State (EOS) for the core of neutron stars (NSs) within the Bayesian statistical approach, we infer the EOS parameters of super-dense neutron-rich nuclear matter from three sets of…
The description of stellar interior remains as a big challenge for the nuclear astrophysics community. The consolidated knowledge is restricted to density regions around the saturation of hadronic matter $\rho _{0} = 2.8\times 10^{14} {\rm\…
The gravitational properties of astrophysical objects depend sensitively on their internal structure. In Newtonian theory, the gravitational potential of a rotating star can be fully described by an infinite number of multipole moments of…
Neutron stars are highly compact astrophysical objects and therefore of utmost relevance to learn about theories of gravity. Whereas the proper equation of state of the nuclear matter inside neutron stars is not yet known, and a wide range…