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

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

doi:10.1088/1475-7516/2022/08/071

Cited by 32 publications

(18 citation statements)

References 77 publications

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“…The TOV Solver Network, on the other hand operates as an efficient emulator for the TOV equations. It is a pre-trained network that outputs the mass-radius (M-R) curve, given any input EoS (further details on the network structure, its training procedure, etc, can be found in [18]). The EoS Network is combined with the well-trained TOV-Solver Network network and optimized in an unsupervised learning scheme.…”

Section: Automatic Differentiationmentioning

confidence: 99%

“…We further discard any reconstructed EoS that does not comply with the causal condition or fails to support a 1.9M ⊙ star. The procedure described above was first tested on several mock M-R data and has been proven to work efficiently [18]. In the next section, we present the results of the reconstructed EoS using the current NS observational data.…”

Section: Automatic Differentiationmentioning

confidence: 99%

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A physics-based neural network reconstruction of the dense matter equation of state from neutron star observables

et al. 2023

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We introduce a novel technique that utilizes a physics-driven deep learning method to reconstruct the dense matter equation of state from neutron star observables, particularly the masses and radii. The proposed framework involves two neural networks: one to optimize the EoS using Automatic Differentiation in the unsupervised learning scheme; and a pre-trained network to solve the Tolman–Oppenheimer–Volkoff (TOV) equations. The gradient-based optimization process incorporates a Bayesian picture into the proposed framework. The reconstructed EoS is proven to be consistent with the results from conventional methods. Furthermore, the resulting tidal deformation is in agreement with the limits obtained from the gravitational wave event, GW170817.

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Section: Automatic Differentiationmentioning

confidence: 99%

Section: Automatic Differentiationmentioning

confidence: 99%

A physics-based neural network reconstruction of the dense matter equation of state from neutron star observables

et al. 2023

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“…In previous works we employed Convolutional Neural Network (CNN) [111] algorithms to detect and infer GW signals from BNS [112,113] and, very recently, from NSBH [114] mergers. Additionally, the use of DNNs as a tool to extract the dense matter EOS from neutron star observations has also been explored in a growing number of studies [54,[115][116][117][118][119][120].…”

Section: Introductionmentioning

confidence: 99%

Translating Neutron Star Observations to Nuclear Symmetry Energy via Deep Neural Networks

Krastev

2022

Galaxies

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One of the most significant challenges involved in efforts to understand the equation of state of dense neutron-rich matter is the uncertain density dependence of the nuclear symmetry energy. In particular, the nuclear symmetry energy is still rather poorly constrained, especially at high densities. On the other hand, detailed knowledge of the equation of state is critical for our understanding of many important phenomena in the nuclear terrestrial laboratories and the cosmos. Because of its broad impact, pinning down the density dependence of the nuclear symmetry energy has been a long-standing goal of both nuclear physics and astrophysics. Recent observations of neutron stars, in both electromagnetic and gravitational-wave spectra, have already constrained significantly the nuclear symmetry energy at high densities. The next generation of telescopes and gravitational-wave observatories will provide an unprecedented wealth of detailed observations of neutron stars, which will improve further our knowledge of the density dependence of nuclear symmetry energy, and the underlying equation of state of dense neutron-rich matter. Training deep neural networks to learn a computationally efficient representation of the mapping between astrophysical observables of neutron stars, such as masses, radii, and tidal deformabilities, and the nuclear symmetry energy allows its density dependence to be determined reliably and accurately. In this work, we use a deep learning approach to determine the nuclear symmetry energy as a function of density directly from observational neutron star data. We show, for the first time, that artificial neural networks can precisely reconstruct the nuclear symmetry energy from a set of available neutron star observables, such as masses and radii as measured by, e.g., the NICER mission, or masses and tidal deformabilities as measured by the LIGO/VIRGO/KAGRA gravitational-wave detectors. These results demonstrate the potential of artificial neural networks to reconstruct the symmetry energy and the equation of state directly from neutron star observational data, and emphasize the importance of the deep learning approach in the era of multi-messenger astrophysics.

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“…To reconstruct the NS EoS in an unbiased manner, we introduce a novel approach that utilizes deep neural networks (DNNs) in the automatic differentiation (AD) framework. This method has been tested on mock data in our previous work [48]. In this work, we study the NS EoS utilizing real NS observational data based on the same approach.…”

mentioning

confidence: 99%

“…We use a less conservative bound in the training data, i.e, the M -R sequences of the EoS must accommodate a neutron star of mass 1.9M . The details of training and validation can be found in our preliminary work [48]. In summary, the well-trained network can efficiently obtain the M -R curve, given an arbitrary EoS.…”

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confidence: 99%

Reconstructing the neutron star equation of state from observational data via automatic differentiation

Soma¹,

Wang²,

Shi³

et al. 2022

Preprint

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The equation of state (EoS) that describes extremely dense matter under strong interactions is not completely understood. One reason is that the first-principle calculations of the EoS at finite chemical potential are challenging in nuclear physics. However, neutron star observables like masses, radii, moment of inertia and tidal deformability are direct probes to the EoS and hence make the EoS reconstruction task feasible. In this work, we present results from a novel deep learning technique that optimizes a parameterized equation of state in the automatic differentiation framework. We predict stellar structures from a pre-trained Tolman-Oppenheimer-Volkoff solver network, given an EoS represented by neural networks. The latest observational data of neutron stars, specifically their masses and radii, are used to implement the chi-square fitting. We optimize the parameters of the neural network EoS by minimizing the error between observations and predictions. The well-trained neural network EoS gives an estimate of the relationship between the pressure and the mass density. The results presented are consistent with those from conventional approaches and the experimental bound on the tidal deformability inferred from the gravitational wave event, GW170817.

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

Cited by 32 publications

References 77 publications

A physics-based neural network reconstruction of the dense matter equation of state from neutron star observables

A physics-based neural network reconstruction of the dense matter equation of state from neutron star observables

Translating Neutron Star Observations to Nuclear Symmetry Energy via Deep Neural Networks

Reconstructing the neutron star equation of state from observational data via automatic differentiation

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