Neural network reconstruction of the dense matter equation of state from neutron star observables

Soma, Shriya; Wang, Lingxiao; Shi, Shengbao; Stöcker, H.; Zhou, Kai

doi:10.48550/arxiv.2201.01756

Cited by 5 publications

(5 citation statements)

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“…[54,55]. Alternative approaches use neural networks for the inference procedure [56][57][58][59][60][61][62]. In our analysis we pay particular attention to the speed of sound inside neutron stars.…”

Section: Introductionmentioning

confidence: 99%

Inference of the sound speed and related properties of neutron stars

Brandes¹,

Weise²,

Kaiser³

2022

Preprint

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Information on the phase structure of strongly interacting matter at high baryon densities can be gained from observations of neutron stars and their detailed analysis. In the present work Bayesian inference methods are used to set constraints on the speed of sound in the interior of neutron stars, based on recent multi-messenger data in combination with limiting conditions from nuclear physics at low densities. Two general parametric representations are introduced for the sound speed cs in order to examine the independence with respect to choices for the parametrisation of Priors. Credible regions for neutron star properties are analysed, in particular with reference to the quest for possible phase transitions in cold dense matter. The evaluation of Bayes factors implies extreme evidence for a violation of the conformal bound, c 2 s ≤ 1/3, inside neutron stars. Given the presently existing data base, it can be concluded that the occurrence of a first-order phase transition in the core of even a two-solar-mass neutron star is unlikely, while a continuous crossover cannot be ruled out. At the same time it is pointed out that the discovery of a superheavy neutron star with a mass M ∼ 2.3 − 2.4 M would strengthen evidence for a phase change in the deep interior of the star.

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Section: Introductionmentioning

confidence: 99%

Inference of the sound speed and related properties of neutron stars

Brandes¹,

Weise²,

Kaiser³

2022

Preprint

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“…In this approach, the enhancement of the baryon chemical potential inside the neutron star can be self-consistently regarded as a gravitational effect. This generalization may provide a way to extract the grand canonical EoS of dense matter via deep learning [33,34].…”

Section: Discussionmentioning

confidence: 99%

Do we need equation of state in curved spacetime for neutron stars?

Li,

Guo,

Zhao

et al. 2022

Preprint

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Neutron stars are regarded as natural laboratories for the study of dense strong interaction matter. The equation of state (EoS) of dense matter computed in flat spacetime is used to predict the structure of neutron stars by solving the Tolman-Oppenheimer-Volkoff (TOV) equation. Recently, it has been reported that the curved spacetime effect or specifically gravitational time dilation effect on the EoS of dense matter leads to a significant increase of the maximum mass limit of neutron stars [Phys. Rev. D 104, 123005 (2021) and JCAP 02, 026 (2021)]. However, in this work, we show that to study the hydrostatic equilibrium of dense matter within the framework of general relativity and relativistic fluid dynamics, the grand canonical EoS of dense matter, p(T, µ), should be the same as that computed in flat spacetime, otherwise it is not consistent with local thermodynamic relations and energy-momentum conservation of the fluid. The gravitation influences the pressure p only through enhancing the temperature T and the chemical potential µ, known as Tolman's law and Klein's law. We rewrite the TOV equation as an alternative version so that the grand canonical EoS computed by using field theoretical methods can be used as a direct input. This may provide a tool to study the grand canonical EoS of dense matter via deep learning.

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“…For example, the inverse TOV mapping of NN was constructed by using the EOS with sound velocity segmentation in Ref. [33], and the reconstructed EOS was implemented with NN at 1 to 7 times the saturated nuclear density in Ref. [34].…”

Section: Introductionmentioning

confidence: 99%

From masses and radii of neutron stars to EOS of nuclear matter through neural network*

Wu 武,

Wen 文

2024

Chinese Phys. C

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The equation of state (EOS) of dense nuclear matter is a key factor to determine the internal structure and properties of neutron stars. However, the EOS of high-density nuclear matter has great uncertainty mainly because the terrestrial nuclear experiments cannot reproduce matter as dense as that in the inner core of a neutron star. Fortunately, continuous improvements in astronomical observations of neutron stars provide the opportunity to inversely constrain the EOS of high-density nuclear matter. A number of methods have been proposed to implement this inverse constraint, such as the Bayesian analysis algorithm, the Lindblom's approach, and so on. Neural network algorithm is an effective new method developed in recent years. By employing a set of isospin-dependent parametric EOSs as the training sample of neural network algorithm, we set up an effective way to reconstruct the EOS with relative accuracy through a few mass-radius data. Based on the obtained neural network algorithms and according to the NICER observations on masses and radii of neutron stars with assumed precision, we get the inversely constrained EOS and further calculate the corresponding macroscopic properties of the neutron star. The results are basically consistent with the constraint on EOS from the Huth et~ al. based on Bayesian analysis. Moreover, the results show that even though the neural network algorithm was obtained by using the finite parameterized EOS as the training set, it is valid for any rational parameter combination of the parameterized EOS model.

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Neural network reconstruction of the dense matter equation of state from neutron star observables

Cited by 5 publications

References 0 publications

Inference of the sound speed and related properties of neutron stars

Inference of the sound speed and related properties of neutron stars

Do we need equation of state in curved spacetime for neutron stars?

From masses and radii of neutron stars to EOS of nuclear matter through neural network*

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