Related papers: Explainable autoencoder for neutron star dense mat…
Recently, in Malik23, a dataset of several EoS for purely nucleonic stellar matter based on a non-linear RMF model prescription, and constrained to properties of nuclear matter, to state-of-the-art chiral effective field theory calculations…
In this lecture, we give a first introduction to neutron stars, based on fundamental physical principles. After outlining their amazing macroscopic properties, as obtained from observations, we infer the extreme conditions of matter in…
The equation of state (EoS) for neutron stars is a crucial topic in astrophysics, nuclear physics, and quantum chromodynamics (QCD), influencing their structure, stability, and observable properties. This review classifies EoS models into…
With an average density higher than the nuclear density, neutron stars (NS) provide a unique test-ground for nuclear physics, quantum chromodynamics (QCD), and nuclear superfluidity. Determination of the fundamental interactions that govern…
Neutron star is the ultimate testing place for the physics of dense nuclear matter. Before the detection of gravitational waves from the merger of binary neutron stars, various nuclear equations of state have been used to estimate the…
We discuss the machine-learning inference and uncertainty quantification for the equation of state (EoS) of the neutron star (NS) matter directly using the NS probability distribution from the observations. We previously proposed a…
The prediction of the equation of state of hot, dense nuclear matter is one of the most complicated and interesting problems in nuclear astrophysics. At the same time, knowledge of it is the basic ingredient for some of the most interesting…
Interpretability is essential in medical imaging to ensure that clinicians can comprehend and trust artificial intelligence models. In this paper, we propose a novel interpretable approach that combines attribute regularization of the…
In view of the great uncertainty of the equation of state (EOS) of high-density nuclear matter, establishing EOS-independent universal relations between global properties of neutron stars provides a practical way to constrain the…
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…
The study of phonon dynamics is pivotal for understanding material properties, yet it faces challenges due to the irreversible information loss inherent in powder inelastic neutron scattering spectra and the limitations of traditional…
At present, the only experimental access to the properties of cold, dense strongly interacting matter is provided by astrophysical observations. Neutron stars are the only known systems in the Universe that reach densities several times…
At the ultra-high densities existing in the core of neutron stars, it is expected that a phase transition from baryonic to deconfined quark matter may occur. Such a phase transition would affect the underlying equation of state (EoS) as…
In the present work we apply a quantum hadrodynamic effective model in the mean-field approximation to the description of neutron stars. We consider an adjustable derivative-coupling model and study the parameter influence on the dynamics…
Autoencoders receive latent models of input data. It was shown in recent works that they also estimate probability density functions of the input. This fact makes using the Bayesian decision theory possible. If we obtain latent models of…
Dark matter (DM) continues to evade direct detection, but neutron stars (NSs) serve as natural laboratories where even a modest DM component can alter their structure. While many studies have examined DM effects on NSs, they often rely on…
Scientific machine learning has enabled the extraction of physical insights and data-driven modeling of high-dimensional spatiotemporal data, yet achieving physically interpretable latent representations and computationally efficient…
Compressed sensing techniques enable efficient acquisition and recovery of sparse, high-dimensional data signals via low-dimensional projections. In this work, we propose Uncertainty Autoencoders, a learning framework for unsupervised…
Nuclear matter and compact neutron stars are studied in the framework of the non-linear derivative (NLD) model which accounts for the momentum dependence of relativistic mean-fields. The generalized form of the energy-momentum tensor is…
Neutron Stars are natural laboratories where fundamental properties of matter under extreme conditions can be explored. Modern nuclear physics input as well as many-body theories are valuable tools which may allow us to improve our…