scholarly journals Constraining the Equation of State of Supranuclear Dense Matter fromXMM‐NewtonObservations of Neutron Stars in Globular Clusters

2007 ◽  
Vol 671 (1) ◽  
pp. 727-733 ◽  
Author(s):  
Natalie A. Webb ◽  
Didier Barret
Keyword(s):  
Equation Of State ◽  
Neutron Stars ◽  
Globular Clusters ◽  
Dense Matter

scholarly journals Pulsars in Globular Clusters

2007 ◽  
Vol 3 (S246) ◽  
pp. 291-300 ◽  
Author(s):  
Scott M. Ransom
Keyword(s):  
Equation Of State ◽  
Neutron Stars ◽  
Globular Clusters ◽  
Main Sequence ◽  
Galactic Disk ◽  
Nuclear Density ◽  
Millisecond Pulsars ◽  
Eccentric Orbits ◽  
The Masses ◽  
Current Record

AbstractGlobular clusters produce orders of magnitude more millisecond pulsars per unit mass than the Galactic disk. Since the first cluster pulsar was uncovered 20 years ago, at least 138 have been identified – most of which are binary millisecond pulsars. Because their origins involve stellar encounters, many of the systems are exotic objects that would never be observed in the Galactic disk. Examples include pulsar-main sequence binaries, extremely rapid rotators (including the current record holder), and millisecond pulsars in highly eccentric orbits. These systems are allowing new probes of the interstellar medium, the equation of state of material at supra-nuclear density, the masses of neutron stars, and globular cluster dynamics.

scholarly journals Equation of state constraints for the cold dense matter inside neutron stars using the cooling tail method

2016 ◽  
Vol 591 ◽  
pp. A25 ◽  
Author(s):  
J. Nättilä ◽  
A. W. Steiner ◽  
J. J. E. Kajava ◽  
V. F. Suleimanov ◽  
J. Poutanen
Keyword(s):  
Equation Of State ◽  
Neutron Stars ◽  
State Constraints ◽  
Dense Matter ◽  
Cold Dense Matter

scholarly journals Equation of state of dense matter and the minimum mass of cold neutron stars

2002 ◽  
Vol 385 (1) ◽  
pp. 301-307 ◽  
Author(s):  
P. Haensel ◽  
J. L. Zdunik ◽  
F. Douchin
Keyword(s):  
Equation Of State ◽  
Neutron Stars ◽  
Cold Neutron ◽  
Dense Matter ◽  
Minimum Mass

scholarly journals Thermal properties of hot and dense matter: Influence of rapid rotation on protoneutron stars, hot neutron stars, and neutron star merger remnants

2021 ◽  
Vol 252 ◽  
pp. 05004
Author(s):  
Polychronis Koliogiannis ◽  
Charalampos Moustakidis
Keyword(s):  
Neutron Star ◽  
Equation Of State ◽  
Neutron Stars ◽  
Nuclear Matter ◽  
Equations Of State ◽  
Interaction Model ◽  
Dense Matter ◽  
Baryon Density ◽  
Dense Nuclear Matter ◽  
Protoneutron Stars

The knowledge of the equation of state is a key ingredient for many dynamical phenomena that depend sensitively on the hot and dense nuclear matter, such as the formation of protoneutron stars and hot neutron stars. In order to accurately describe them, we construct equations of state at FInite temperature and entropy per baryon for matter with varying proton fractions. This procedure is based on the momentum dependent interaction model and state-of-the-art microscopic data. In addition, we investigate the role of thermal and rotation effects on microscopic and macroscopic properties of neutron stars, including the mass and radius, the frequency, the Kerr parameter, the central baryon density, etc. The latter is also connected to the hot and rapidly rotating remnant after neutron star merger. The interplay between these quantities and data from late observations of neutron stars, both isolated and in matter of merging, could provide useful insight and robust constraints on the equation of state of nuclear matter.

EQUATION OF STATE OF DENSE MATTER IN NEUTRON STARS

2008 ◽  
Author(s):  
SLAVOMÍR ČERNÝ ◽  
JIŘINA ŘÍKOVSKÁ STONE ◽  
ZDENĚK STUCHLÍK ◽  
STANISLAV HLEDÍK
Keyword(s):  
Equation Of State ◽  
Neutron Stars ◽  
Dense Matter

scholarly journals Neural network reconstruction of the dense matter equation of state derived from the parameters of neutron stars

2020 ◽  
Vol 642 ◽  
pp. A78 ◽  
Author(s):  
F. Morawski ◽  
M. Bejger
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Neutron Star ◽  
Equation Of State ◽  
Neutron Stars ◽  
Equations Of State ◽  
Dense Matter ◽  
Data Set ◽  
Global Parameters

Context. Neutron stars are currently studied with an rising number of electromagnetic and gravitational-wave observations, which will ultimately allow us to constrain the dense matter equation of state and understand the physical processes at work within these compact objects. Neutron star global parameters, such as the mass and radius, can be used to obtain the equation of state by directly inverting the Tolman-Oppenheimer-Volkoff equations. Here, we investigate an alternative approach to this procedure. Aims. The aim of this work is to study the application of the artificial neural networks guided by the autoencoder architecture as a method for precisely reconstructing the neutron star equation of state, using their observable parameters: masses, radii, and tidal deformabilities. In addition, we study how well the neutron star radius can be reconstructed using only the gravitational-wave observations of tidal deformability, that is, using quantities that are not related in any straightforward way. Methods. The application of an artificial neural network in the equation-of-state reconstruction exploits the non-linear potential of this machine learning model. Since each neuron in the network is basically a non-linear function, it is possible to create a complex mapping between the input sets of observations and the output equation-of-state table. Within the supervised training paradigm, we construct a few hidden-layer deep neural networks on a generated data set, consisting of a realistic equation of state for the neutron star crust connected with a piecewise relativistic polytropes dense core, with its parameters representative of state-of-the art realistic equations of state. Results. We demonstrate the performance of our machine-learning implementation with respect to the simulated cases with a varying number of observations and measurement uncertainties. Furthermore, we study the impact of the neutron star mass distributions on the results. Finally, we test the reconstruction of the equation of state trained on parametric polytropic training set using the simulated mass–radius and mass–tidal-deformability sequences based on realistic equations of state. Neural networks trained with a limited data set are capable of generalising the mapping between global parameters and equation-of-state input tables for realistic models.

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