Accurate extractions of the detected gravitational wave (GW) signal waveforms are essential to validate a detection and to probe the astrophysics behind the sources producing the GWs. This however could be difficult in realistic scenarios where the s
ignals detected by existing GW detectors could be contaminated with non-stationary and non-Gaussian noise. While the performance of existing waveform extraction methods are optimal, they are not fast enough for online application, which is important for multi-messenger astronomy. In this paper, we demonstrate that a deep learning architecture consisting of Convolutional Neural Network and bidirectional Long Short-Term Memory components can be used to extract binary black hole (BBH) GW waveforms from realistic noise in a few milli-seconds. We have tested our network systematically on injected GW signals, with component masses uniformly distributed in the range of 10 to 80 solar masses, on Gaussian noise and LIGO detector noise. We find that our model can extract GW waveforms with overlaps of more than 0.95 with pure Numerical Relativity templates for signals with signal-to-noise ratio (SNR) greater than six, and is also robust against interfering glitches. We then apply our model to all ten detected BBH events from the first (O1) and second (O2) observation runs, obtaining greater than 0.97 overlaps for all ten extracted BBH waveforms with the corresponding pure templates. We discuss the implication of our result and its future applications to GW localization and mass estimation.
This paper presents the SPIIR pipeline used for public alerts during the third advanced LIGO and Virgo observation run (O3 run). The SPIIR pipeline uses infinite impulse response (IIR) filters to perform extremely low-latency matched filtering and th
is process is further accelerated with graphics processing units (GPUs). It is the first online pipeline to select candidates from multiple detectors using a coherent statistic based on the maximum network likelihood ratio statistic principle. Here we simplify the derivation of this statistic using the singular-value-decomposition (SVD) technique and show that single-detector signal-to-noise ratios from matched filtering can be directly used to construct the statistic for each sky direction. Coherent searches are in general more computationally challenging than coincidence searches due to extra search over sky direction parameters. The search over sky directions follows an embarrassing parallelization paradigm and has been accelerated using GPUs. The detection performance is reported using a segment of public data from LIGO-Virgos second observation run. We demonstrate that the median latency of the SPIIR pipeline is less than 9 seconds, and present an achievable roadmap to reduce the latency to less than 5 seconds. During the O3 online run, SPIIR registered triggers associated with 38 of the 56 non-retracted public alerts. The extreme low-latency nature makes it a competitive choice for joint time-domain observations, and offers the tantalizing possibility of making public alerts prior to the merger phase of binary coalescence systems involving at least one neutron star.
Gravitational wave (GW) data can be used to test the parity symmetry of gravity by investigating the difference between left-hand and right-hand circular polarization modes. In this article, we develop a method to decompose the circular polarizations
of GWs produced during the inspiralling stage of compact binaries, with the help of stationary phase approximation. The foremost advantage is that this method is simple, clean, independent of GW waveform, and is applicable to the existing detector network. Applying it to the mock data, we test the parity symmetry of gravity by constraining the velocity birefringence of GWs. If a nearly edge-on binary neutron-stars with observed electromagnetic counterparts at 40 Mpc is detected by the second-generation detector network, one could derive the model-independent test on the parity symmetry in gravity: the lower limit of the energy scale of parity violation can be constrained within $mathcal{O}(10^4{rm eV})$.
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 its 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 solar mass 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 analytic
We examine how fast radio burst (FRB)-like signals predicted to be generated during the merger of a binary neutron star (BNS) may be detected in low-frequency radio observations triggered by the aLIGO/Virgo gravitational wave detectors. The rapidity,
directional accuracy, and sensitivity of follow-up observations with the Murchison Widefield Array (MWA) are considered. We show that with current methodology, the rapidity criteria fails for triggered MWA observations above 136 MHz for BNS mergers within the aLIGO/Virgo horizon, for which little dispersive delay is expected. A calculation of the expected reduction in response time by triggering on `negative latency alerts from aLIGO/Virgo observations of gravitational waves generated by the BNS inspiral is presented. This allows for observations up to 300 MHz where the radio signal is expected to be stronger. To compensate for the poor positional accuracy expected from these alerts, we propose a new MWA observational mode that is capable of viewing one quarter of the sky. We show the sensitivity of this mode is sufficient to detect an FRB-like burst from an event similar to GW170817 if it occurred during the ongoing aLIGO/Virgo third science run (O3).
We use the Fisher information matrix to investigate the angular resolution and luminosity distance uncertainty for coalescing binary neutron stars (BNSs) and neutron star-black hole binaries (NSBHs) detected by the third-generation (3G) gravitational
-wave (GW) detectors. Our study focuses on an individual 3G detector and a network of up to four 3G detectors at different locations including the US, Europe, China and Australia for the proposed Einstein Telescope (ET) and Cosmic Explorer (CE) detectors. We in particular examine the effect of the Earths rotation, as GW signals from BNS and low mass NSBH systems could be hours long for 3G detectors. We find that, a time-dependent antenna beam-pattern function can help better localize BNS and NSBH sources, especially those edge-on ones. The medium angular resolution for one ET-D detector is around 150 deg$^2$ for BNSs at a redshift of $z=0.1$. The medium angular resolution for a network of two CE detectors in the US and Europe respectively is around 20 deg$^2$ at $z=0.2$ for the simulated BNS and NSBH samples. While for a network of two ET-D detectors, the similar angular resolution can be achieved at a much higher redshift of $z=0.5$. The angular resolution of a network of three detectors is mainly determined by the baselines between detectors regardless of the CE or ET detector type. We discuss the implications of our results to constrain the Hubble constant $H_0$, the deceleration parameter $q_0$ and the equation-of-state (EoS) of dark energy. We find that in general, if 10 BNSs or NSBHs at $z=0.1$ with known redshifts are detected, $H_0$ can be measured with an accuracy of $0.9%$. If 1000 face-on BNSs at $z<2$ are detected with known redshifts, we are able to achieve $Delta q_0=0.002$, or $Delta w_0=0.03$ and $Delta w_a=0.2$ for dark energy.(Abridged version).
We report for the first time a method-independent geometrical expression for the angular resolution of an arbitrary network of interferometric gravitational wave (GW) detectors when the arrival-time of a GW is unknown. We discuss the implications of
our results on how to improve angular resolutions of a GW network and on improvements of localization methods. An example of an improvement to the null-stream localization method for GWs of unknown waveforms is demonstrated.
The planned Laser Interferometer Space Antenna (LISA) is expected to detect gravitational wave signals from ~100 extreme-mass-ratio inspirals (EMRIs) of stellar-mass compact objects into massive black holes. The long duration and large parameter spac
e of EMRI signals makes data analysis for these signals a challenging problem. One approach to EMRI data analysis is to use time-frequency methods. This consists of two steps: (i) searching for tracks from EMRI sources in a time-frequency spectrogram, and (ii) extracting parameter estimates from the tracks. In this paper we discuss the results of applying these techniques to the latest round of the Mock LISA Data Challenge, Round 1B. This analysis included three new techniques not used in previous analyses: (i) a new Chirp-based Algorithm for Track Search for track detection; (ii) estimation of the inclination of the source to the line of sight; (iii) a Metropolis-Hastings Monte Carlo over the parameter space in order to find the best fit to the tracks.
Extreme-mass-ratio inspirals (EMRIs) of ~ 1-10 solar-mass compact objects into ~ million solar-mass massive black holes can serve as excellent probes of strong-field general relativity. The Laser Interferometer Space Antenna (LISA) is expected to det
ect gravitational wave signals from apprxomiately one hundred EMRIs per year, but the data analysis of EMRI signals poses a unique set of challenges due to their long duration and the extensive parameter space of possible signals. One possible approach is to carry out a search for EMRI tracks in the time-frequency domain. We have applied a time-frequency search to the data from the Mock LISA Data Challenge (MLDC) with promising results. Our analysis used the Hierarchical Algorithm for Clusters and Ridges to identify tracks in the time-frequency spectrogram corresponding to EMRI sources. We then estimated the EMRI source parameters from these tracks. In these proceedings, we discuss the results of this analysis of the MLDC round 1.3 data.
