相关论文: Insights into neutron star equation of state by ma…
A novel equation of state used for analysis of the heavy ion collision experimental data is generalized to also describe the matter inside neutron stars. This approach differs from others by including an induced surface tension caused by…
In this work, we propose a meta-modelling technique to nuclear matter on the basis of a relativistic density functional with density-dependent couplings. Identical density dependence for the couplings both in the isoscalar and isovector…
Progress in the application of machine learning techniques to the prediction of solid-state and molecular materials properties has been greatly facilitated by the development state-of-the-art feature representations and novel deep learning…
Learning in neural networks critically hinges on the intricate geometry of the loss landscape associated with a given task. Traditionally, most research has focused on finding specific weight configurations that minimize the loss. In this…
Motivated by a recent astrophysical measurement of the pressure of cold matter above nuclear-matter saturation density, we compute the equation of state of neutron star matter using accurately calibrated relativistic models. The uniform…
The sensitivity of nuclear symmetry energy elements at the saturation density to the binding energies of ultra neutron-rich nuclei (neutron to proton ratio $\sim$ 2) and the maximum mass of neutron star is explored within a relativistic…
The low density nuclear matter equation of state is strongly constrained by nuclear properties, however, for constraining the high density equation of state it is necessary to resort to indirect information obtained from the observation of…
Nuclear matter and neutron stars are studied in the framework of an extended relativistic mean-field (RMF) model with higher-order derivative and density dependent couplings of nucleons to the meson fields. The derivative couplings lead to…
We present a physics-informed Bayesian neural-network framework to infer neutron-star equations of state from theoretical priors and to propagate the associated uncertainties to stellar observables. Trained on a large and representative…
We construct a class of nuclear equations of state based on a schematic potential model, that originates from the work of Prakash et. al. \cite{Prakash-88}, which reproduce the results of most microscopic calculations. The equations of…
As some of the most compact stellar objects in the universe, neutron stars are unique cosmic laboratories. The study of neutron stars provides an ideal theoretical testbed for investigating both physics at supra-nuclear densities as well as…
We present a Bayesian analysis to constrain the equation of state of dense nucleonic matter by exploiting the available data from symmetric nuclear matter at saturation and from observations of compact X-ray sources and from the…
The rapid increase in the number and precision of astrophysical probes of neutron stars in recent years allows for the inference of their equation of state. Observations target different macroscopic properties of neutron stars which vary…
We explore supervised machine learning methods in extracting the non-linear maps between neutron stars (NS) observables and the equation of state (EoS) of nuclear matter. Using a Taylor expansion around saturation density, we have generated…
The general behavior of the nuclear equation of state (EOS), relevant for the description of neutron stars (NS), is studied within a relativistic mean field description of nuclear matter. Different formulations, both with density dependent…
We review the current status and recent progress of microscopic many-body approaches and phenomenological models, which are employed to construct the equation of state of neutron stars. The equation of state is relevant for the description…
Recent developments in the theory of pure neutron matter and experiments concerning the symmetry energy of nuclear matter, coupled with recent measurements of high-mass neutron stars, now allow for relatively tight constraints on the…
We propose to use deep learning to estimate parameters in statistical models when standard likelihood estimation methods are computationally infeasible. We show how to estimate parameters from max-stable processes, where inference is…
We introduce a new framework for quantifying correlated uncertainties of the infinite-matter equation of state derived from chiral effective field theory ($\chi$EFT). Bayesian machine learning via Gaussian processes with physics-based…
In view of new constraints put forth by recent observations and measurements in the realm of astrophysics and nuclear physics, we update the non-linear realization of the sigma model as to reflect such constraints. By doing this, we obtain…