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Register a new userDetection and Parameter Estimation of Gravitational Waves from Binary Neutron-Star Mergers in Real LIGO Data using Deep Learning
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One of the key challenges of real-time detection and parameter estimation of gravitational waves from compact binary mergers is the computational cost of conventional matched-filtering and Bayesian inference approaches. In particular, the application of these methods to the full signal parameter space available to the gravitational-wave detectors, and/or real-time parameter estimation is computationally prohibitive. On the other hand, rapid detection and inference are critical for prompt follow-up of the electromagnetic and astro-particle counterparts accompanying important transients, such as binary neutron-star and black-hole neutron-star mergers. Training deep neural networks to identify specific signals and learn a computationally efficient representation of the mapping between gravitational-wave signals and their parameters allows both detection and inference to be done quickly and reliably, with high sensitivity and accuracy. In this work we apply a deep-learning approach to rapidly identify and characterize transient gravitational-wave signals from binary neutron-star mergers in real LIGO data. We show for the first time that artificial neural networks can promptly detect and characterize binary neutron star gravitational-wave signals in real LIGO data, and distinguish them from noise and signals from coalescing black-hole binaries. We illustrate this key result by demonstrating that our deep-learning framework classifies correctly all gravitational-wave events from the Gravitational-Wave Transient Catalog, GWTC-1 [Phys. Rev. X 9 (2019), 031040]. These results emphasize the importance of using realistic gravitational-wave detector data in machine learning approaches, and represent a step towards achieving real-time detection and inference of gravitational waves.
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The discovery of the astrophysical events GW150926 and GW151226 has experimentally confirmed the existence of gravitational waves (GW) and has demonstrated the existence of binary stellar-mass black hole systems. This finding marks the beginning of a new era that will reveal unexpected features of our universe. This work presents a basic insight to the fundamental theory of GW emitted by inspiral binary systems and describes the scientific and technological efforts developed to measure these waves using the interferometer-based detector called LIGO. Subsequently, the work presents a comprehensive data analysis methodology based on the matched filter algorithm, which aims to recovery GW signals emitted by inspiral binary systems of astrophysical sources. This algorithm was evaluated with freely available LIGO data containing injected GW waveforms. Results of the experiments performed to assess detection accuracy showed the recovery of 85% of the injected GW.
Neutron star binary mergers are strong sources of gravitational waves (GWs). Promising electromagnetic counterparts are short gamma-ray bursts (GRBs) but the emission is highly collimated. We propose that the scattering of the long-lasting plateau emission in short GRBs by the merger ejecta produces nearly isotropic emission for $sim 10^4$ s with flux $10^{-13}-10^{-10}$ erg cm$^{-2}$ s$^{-1}$ at 100 Mpc in X-ray. This is detectable by Swift XRT and wide field X-ray detectors such as ISS-Lobster, Einstein Probe, eROSITA and WF-MAXI, which are desired by the infrared and optical follow-ups to localize and measure the distance to the host galaxy. The scattered X-rays obtain linear polarization, which correlates with the jet direction, X-ray luminosity and GW polarizations. The activity of plateau emission is also a natural energy source of a macronova (or kilonova) detected in short GRB 130603B without the $r$-process radioactivity.
The groundbreaking discoveries of gravitational waves from binary black-hole mergers and, most recently, coalescing neutron stars started a new era of Multi-Messenger Astrophysics and revolutionized our understanding of the Cosmos. Machine learning techniques such as artificial neural networks are already transforming many technological fields and have also proven successful in gravitational-wave astrophysics for detection and characterization of gravitational-wave signals from binary black holes. Here we use a deep-learning approach to rapidly identify transient gravitational-wave signals from binary neutron star mergers in noisy time series representative of typical gravitational-wave detector data. Specifically, we show that a deep convolution neural network trained on 100,000 data samples can rapidly identify binary neutron star gravitational-wave signals and distinguish them from noise and signals from merging black hole binaries. These results demonstrate the potential of artificial neural networks for real-time detection of gravitational-wave signals from binary neutron star mergers, which is critical for a prompt follow-up and detailed observation of the electromagnetic and astro-particle counterparts accompanying these important transients.
Significant human and observational resources have been dedicated to electromagnetic followup of gravitational-wave events detected by Advanced LIGO and Virgo. As the sensitivity of LIGO and Virgo improves, the rate of sources detected will increase. Margalit & Metzger (2019; arXiv:1904.11995) have suggested that it may be necessary to prioritize observations of future events. Optimal prioritization requires a rapid measurement of a gravitational-wave events masses and spins, as these can determine the nature of any electromagnetic emission. We extend the relative binning method of Zackay et al. (2018; arXiv:1806.08792) to a coherent detector-network statistic. We show that the method can be seeded from the output of a matched-filter search and used in a Bayesian parameter measurement framework to produce marginalized posterior probability densities for the sources parameters within 20 minutes of detection on 32 CPU cores. We demonstrate that this algorithm produces unbiased estimates of the parameters with the same accuracy as running parameter estimation using the standard gravitational-wave likelihood. We encourage the adoption of this method in future LIGO-Virgo observing runs to allow fast dissemination of the parameters of detected events so that the observing community can make best use of its resources.
We present results of an all-sky search for continuous gravitational waves (CWs), which can be produced by fast-spinning neutron stars with an asymmetry around their rotation axis, using data from the second observing run of the Advanced LIGO detectors. We employ three different semi-coherent methods ($textit{FrequencyHough}$, $textit{SkyHough}$, and $textit{Time-Domain $mathcal{F}$-statistic}$) to search in a gravitational-wave frequency band from 20 to 1922 Hz and a first frequency derivative from $-1times10^{-8}$ to $2times10^{-9}$ Hz/s. None of these searches has found clear evidence for a CW signal, so we present upper limits on the gravitational-wave strain amplitude $h_0$ (the lowest upper limit on $h_0$ is $1.7times10^{-25}$ in the 123-124 Hz region) and discuss the astrophysical implications of this result. This is the most sensitive search ever performed over the broad range of parameters explored in this study.
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