Related papers: Detecting Hyperons in neutron stars -- a machine l…
In this work we delve into the temperature-dependent Equation of State (EoS) of baryonic matter within the framework of the FSU2H$^*$ hadronic model, which comprehensively incorporates hyperons and is suitable for relativistic simulations…
Machine Learning algorithms are good tools for both classification and prediction purposes. These algorithms can further be used for scientific discoveries from the enormous data being collected in our era. We present ways of discovering…
The nuclear matter equation of state is relatively well constrained at sub-saturation densities thanks to the knowledge from nuclear physics. However, studying its behavior at supra-saturation densities is a challenging task. Fortunately,…
In this review we highlight a few physical properties of neutron stars and their theoretical treatment inasmuch as they can be useful for nuclear and particle physicists concerned with matter at finite density (and newly, temperature).…
The radio astronomy community is rapidly adopting deep learning techniques to deal with the huge data volumes expected from the next generation of radio observatories. Bayesian neural networks (BNNs) provide a principled way to model…
Investigations of thermal evolution of neutron stars with hyperon cores require neutrino emissivities for many neutrino reactions involving strongly degenerate particles (nucleons, hyperons, electrons, muons). We calculate the angular…
Within a density-dependent relativistic mean-field model using in-medium meson-hadron coupling constants and meson masses, we explore effects of in-medium hyperon interactions on properties of neutron stars. It is found that the hyperonic…
We investigate the tidal deformability of a superfluid neutron star. We calculate the equilibrium structure in the general relativistic two-fluid formalism with entrainment effect where we take neutron superfluid as one fluid and the other…
Detection of the characteristic spectrum of pulsating neutron stars can be a powerful tool not only to probe the nuclear equation of state, but also to test modifications to general relativity. However, the shift in the oscillation spectrum…
We present a detailed investigation on the structure of neutron stars, incorporating the presence of hyperons within a relativistic model under the mean-field approximation. Employing coupling constants derived from QCD sum rules, we…
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…
Models of neutron stars (NSs) with hyperon cores are constructed with an effective chiral model in mean-field approximation. The hyperon couplings are fixed by reproducing their experimentally determined binding energies. The impact of…
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…
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…
Matter properties at the intermediate densities are still unknown to us. In this work, we use a neural network approach to study matter at intermediate densities to analyze the variation of the speed of sound and the measure of trace…
Machine Learning is an efficient method for analyzing and interpreting the increasing amount of astronomical data that is available. In this study, we show, a pedagogical approach that should benefit anyone willing to experiment with Deep…
We present a novel approach for classifying stars as binary or exoplanet using deep learning techniques. Our method utilizes feature extraction, wavelet transformation, and a neural network on the light curves of stars to achieve…
In the last decade, over a million stars were monitored to detect transiting planets. Manual interpretation of potential exoplanet candidates is labor intensive and subject to human error, the results of which are difficult to quantify.…
In the present study we analyse the effect of the density dependence of the symmetry energy on the hyperonic content of neutron stars within a relativistic mean field description of stellar matter. For the $\Lambda$-hyperon, we consider…
Neutron stars are astrophysical laboratories of many extremes of physics. Their rich phenomenology provides insights into the state and composition of matter at densities which cannot be reached in terrestrial experiments. Since the core of…