Simulations of inspiraling and merging double neutron stars using the Spectral Einstein Code

We report results of numerical relativity simulations of binary neutron star mergers. We perform simulations for new 26 non-spinning binary models with 6 grid resolutions using an adaptive mesh refinement numerical relativity code SACRA-MPI. The finest grid spacing is ≈ 64 m. First, we derive long-term high-precision inspiral gravitational waveforms for calibrating the SACRA gravitational waveform template. We find that the accumulated gravitational-wave phase error due to the finite grid resolution is less than 0.5 radian during more than 200 radian phase evolution irrespective of the models. We also find that the gravitational-wave phase error for a model with a tabulated equation of state is comparable to that for a piece-wise polytropic equation of state. Then we calibrate the proposed universal relations between the post-merger gravitational wave signal and tidal deformability/neutron star radius in the literature. We find that they suffer from systematics and many relations proposed as universal are not very universal. We also propose improved fitting formulae. Finally, we validate the SACRA gravitational waveform template which will be used to extract tidal deformability from gravitational wave observation and find that accuracy of our waveform modeling is 0.1 radian in the gravitational-wave phase and 20% in the gravitationalwave amplitude up to the gravitational-wave frequency 1000 Hz.

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“…The phase error of O(1) radian could be an obstacle to construct a highquality inspiral gravitational waveform template (see also Refs. [43,44]).…”

Section: Introductionmentioning

confidence: 99%

Sub-radian-accuracy gravitational waves from coalescing binary neutron stars in numerical relativity. II. Systematic study on the equation of state, binary mass, and mass ratio

et al. 2020

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“…[20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35] In order to test this, we first simulate the GW signals with the published BBH parameters as listed in Table VI for GW150914 and Tables VII and VIII for GW170818 and GW170823 events. Next, we consider a large number of sectors: 1024, 2048, 4096 and train our model with samples of SNR in the range of [20][21][22][23][24][25] for the GW150914 test sample and with SNR in the range of [10][11][12][13][14][15] for GW170818 and GW170823 test samples. We then test our model's classification on them and use the probability assigned to each sector by our ANN for these three test samples as ranking statistics from which we obtain the cumulative frequency distribution of the probabilities and get the 90% and 50% contours in the same way as in [93].…”

Section: Test Results For Gw150914 Gw170818 and Gw170823-like Soumentioning

confidence: 99%

“…composition of these compact objects. [14][15][16][17][18][19].LIGO and Virgo began their third observation run (O3) in April, 2019 and have already detected several new GW signals. The technique used by search pipelines to identify and characterize GW signals from noise is matched filtering, which uses a bank of template waveforms of different component masses and spins [20][21][22][23][24][25][26][27].…”

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confidence: 99%

Using deep learning to localize gravitational wave sources

et al. 2019

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Deep Learning algorithms, in particular neural networks have been steadily gaining popularity among the gravitational wave community for the last few years. The reliability and accuracy of Deep Learning approaches in gravitational wave detection, parameter estimation and glitch classification have already been proved and verified by several groups in recent years. In this paper, we report on the construction of a deep Artificial Neural Network (ANN) to localize simulated gravitational wave signals in the sky with high accuracy. We have modelled the sky as a sphere and have considered cases where the sphere is divided into 18, 50, 128, 1024, 2048 and 4096 sectors. The sky direction of the gravitational wave source is estimated by classifying the signal into one of these sectors based on it's right ascension and declination values for each of these cases. In order to do this, we have injected simulated binary black hole gravitational wave signals of component masses sampled uniformly between 30-80 M into Gaussian noise and used the whitened strain values to obtain the input features for training our ANN. We input features such as the delays in arrival times, phase differences and amplitude ratios at each of the three detectors Hanford, Livingston and Virgo, from the raw time-domain strain values as well as from analytical versions of these signals, obtained through Hilbert transformation. We show that our model is able to classify gravitational wave samples, not used in the training process, into their correct sectors with very high accuracy (> 90%) for coarse angular resolution using 18, 50 and 128 sectors. We also test our localization on test samples with injection parameters of the published LIGO binary black hole merger events GW150914, GW170818 and GW170823 for 1024, 2048 and 4096 sectors and compare the result with that from BAYESTAR and Parameter Estimation (PE). In addition, we report that the time taken by our model to localize one GW signal is around 0.018 secs on 14 Intel Xeon CPU cores. composition of these compact objects. [14][15][16][17][18][19].LIGO and Virgo began their third observation run (O3) in April, 2019 and have already detected several new GW signals. The technique used by search pipelines to identify and characterize GW signals from noise is matched filtering, which uses a bank of template waveforms of different component masses and spins [20][21][22][23][24][25][26][27]. These waveform models cover the inspiral, merger and ringdown phases of a compact binary coalescence and are generated by combining post-Newtonian theory [28][29][30][31], the effective-one-body formalism [32] and numerical relativity simulations [33]. While this approach has been very successful, the algorithms used are computationally expensive due to the fact that they probe a large parameter space with increasingly longerduration waveforms, as the low-frequency sensitivity of the interferometers improves. Also, future observation runs will involve more detectors running simultaneously over longer periods of time, th...

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“…While the numerical modeling of black hole collisions has evolved rapidly over the last decade, the modeling of astrophysical objects that involve matter, such as neutron star collisions, has progressed at a lower pace [30,[38][39][40][41]. The different timescales involved in these complex systems, and the need to couple Einstein's field equations with magnetohydrodynamics and microphysics is a challenging endeavor.…”

Section: Gravitational Wave Astrophysics: From Theoretical Insights Tmentioning

confidence: 99%

Supporting High-Performance and High-Throughput Computing for Experimental Science

Huerta

Haas

Jha

et al. 2019

Comput Softw Big Sci

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The advent of experimental science facilitiesinstruments and observatories, such as the Large Hadron Collider (LHC), the Laser Interferometer Gravitational Wave Observatory (LIGO), and the upcoming Large Synoptic Survey Telescope (LSST)-has brought about challenging, largescale computational and data processing requirements. Traditionally, the computing infrastructures to support these facility's requirements were organized into separate infrastructure that supported their high-throughput needs and those that supported their high-performance computing needs. We argue that in order to enable and accelerate scientific discovery at the scale and sophistication that is now needed, this separation between High-Performance Computing (HPC) and High-Throughput Computing (HTC) must be bridged and an

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Simulations of inspiraling and merging double neutron stars using the Spectral Einstein Code

Cited by 49 publications

References 138 publications

Sub-radian-accuracy gravitational waves from coalescing binary neutron stars in numerical relativity. II. Systematic study on the equation of state, binary mass, and mass ratio

Sub-radian-accuracy gravitational waves from coalescing binary neutron stars in numerical relativity. II. Systematic study on the equation of state, binary mass, and mass ratio

Using deep learning to localize gravitational wave sources

Supporting High-Performance and High-Throughput Computing for Experimental Science

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