Deep Learning for Gravitational-Wave Data Analysis: A Resampling White-Box Approach

Morales, Manuel D.; Antelis, Javier M.; Moreno, Claudia; Nesterov, Alexander I.

doi:10.3390/s21093174

Cited by 10 publications

(7 citation statements)

References 62 publications

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“…[103][104][105][106][107][108][109][110][111][112][113]. In particular, DL has been applied in GW data analysis for the detection [114][115][116][117][118][119][120][121], parameter estimation [122,123], and denoising [124] of GW signals from compact binary mergers. In previous works [125,126], we also pioneered the use of DL methods, specifically Convolutional Neural Network (CNN) [127] algorithms, for the detection and inference of GW signals from BNS mergers embedded in both Gaussian and realistic LIGO noise.…”

Section: Introductionmentioning

confidence: 99%

“…Deep neural networks (DNNs) [96,97] have garnered attention in the research community, where deep learning (DL) algorithms have displayed exceptional proficiency in tasks such as image recognition [98] and natural language processing [99]. Furthermore, these techniques have been applied to various physics and astrophysics domains, including the analysis of GW data for detection [100][101][102][103][104][105][106][107], parameter estimation [108,109], and denoising [110]. 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.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Krastev

2022

Galaxies

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“…In 2015 two GW observatories, LIGO Scientific Collaboration and Virgo Collaboration, first observed GW spacetime deviation using large interferometers [1]. For such data analyses of GW detection, several DL methods have been already developed for extracting signals from raw data contaminated by noise [2][3][4]. But the DL reconstruction of metric tensors g αβ for detected spacetimes has not yet been extensively explored as a conceptual issue in GR.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Metric Detectors in General Relativity

Katsube¹,

Tam²,

Hotta³

et al. 2022

Preprint

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We consider conceptual issues of deep learning (DL) for metric detectors using test particle geodesics in curved spacetimes. Advantages of DL metric detectors are emphasized from a view point of general coordinate transformations. Two given metrics (two spacetimes) are defined to be conneted by a DL isometry if their geodesic image data cannot be discriminated by any DL metric detector at any time. The fundamental question of when the DL isometry appears is extensively explored. If the two spacetimes connected by the DL isometry are in superposition of quantum gravity theory, the post-measurement state may be still in the same superposition even after DL metric detectors observe the superposed state. We also demonstrate metric-detection DL's in 2+1 dimensional anti-de Sitter (AdS) spacetimes to estimate the cosmological constants and Brown-Henneaux charges. In the AdS/CFT correspondence dictionary, it may be expected that such metric detectors in the AdS bulk region correspond to quantum measurement devices in the CFT at the AdS boundary.

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“…[103][104][105][106][107][108][109][110][111][112][113]. In particular, DL has been applied in GW data analysis for detection [114][115][116][117][118][119][120][121], parameter estimation [122,123], and denoising [124] of GW signals from compact binary mergers. In previous works [125,126], we also pioneered the use of DL methods, specifically Convolutional Neural Network (CNN) [127] algorithms, for detection and inference of GW signals from binary neutron star (BNS) mergers embedded in both Gaussian and realistic LIGO noise.…”

Section: Introductionmentioning

confidence: 99%

Translating neutron star observations to nuclear symmetry energy via artificial neural networks

Krastev

2021

Preprint

View full text Add to dashboard Cite

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 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 longstanding 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 gravitationalwave 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 those 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.

show abstract

Deep Learning for Gravitational-Wave Data Analysis: A Resampling White-Box Approach

Cited by 10 publications

References 62 publications

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

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

Deep Learning Metric Detectors in General Relativity

Translating neutron star observations to nuclear symmetry energy via artificial neural networks

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