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Register a new userMatching matched filtering with deep networks in gravitational-wave astronomy
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We report on the construction of a deep convolutional neural network that can reproduce the sensitivity of a matched-filtering search for binary black hole gravitational-wave signals. The standard method for the detection of well modeled transient gravitational-wave signals is matched filtering. However, the computational cost of such searches in low latency will grow dramatically as the low frequency sensitivity of gravitational-wave detectors improves. Convolutional neural networks provide a highly computationally efficient method for signal identification in which the majority of calculations are performed prior to data taking during a training process. We use only whitened time series of measured gravitational-wave strain as an input, and we train and test on simulated binary black hole signals in synthetic Gaussian noise representative of Advanced LIGO sensitivity. We show that our network can classify signal from noise with a performance that emulates that of match filtering applied to the same datasets when considering the sensitivity defined by Reciever-Operator characteristics.
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This white paper describes the research and development needed over the next decade to realize Cosmic Explorer, the U.S. node of a future third-generation detector network that will be capable of observing and characterizing compact gravitational-wave sources to cosmological redshifts.
Coincident observations with gravitational wave (GW) detectors and other astronomical instruments are in the focus of the experiments with the network of LIGO, Virgo and GEO detectors. They will become a necessary part of the future GW astronomy as the next generation of advanced detectors comes online. The success of such joint observations directly depends on the source localization capabilities of the GW detectors. In this paper we present studies of the sky localization of transient sources with the future advanced detector networks and describe their fundamental properties. By reconstructing sky coordinates of ad hoc signals injected into simulated detector noise we study the accuracy of the source localization and its dependence on the strength of injected signals, waveforms and network configurations.
With the advent of gravitational wave astronomy, techniques to extend the reach of gravitational wave detectors are desired. In addition to the stellar-mass black hole and neutron star mergers already detected, many more are below the surface of the noise, available for detection if the noise is reduced enough. Our method (DeepClean) applies machine learning algorithms to gravitational wave detector data and data from on-site sensors monitoring the instrument to reduce the noise in the time-series due to instrumental artifacts and environmental contamination. This framework is generic enough to subtract linear, non-linear, and non-stationary coupling mechanisms. It may also provide handles in learning about the mechanisms which are not currently understood to be limiting detector sensitivities. The robustness of the noise reduction technique in its ability to efficiently remove noise with no unintended effects on gravitational-wave signals is also addressed through software signal injection and parameter estimation of the recovered signal. It is shown that the optimal SNR ratio of the injected signal is enhanced by $sim 21.6%$ and the recovered parameters are consistent with the injected set. We present the performance of this algorithm on linear and non-linear noise sources and discuss its impact on astrophysical searches by gravitational wave detectors.
On a time scale of years to decades, gravitational wave (GW) astronomy will become a reality. Low frequency (nanoHz) GWs are detectable through long-term timing observations of the most stable pulsars. Radio observatories worldwide are currently carrying out observing programmes to detect GWs, with data sets being shared through the International Pulsar Timing Array project. One of the most likely sources of low frequency GWs are supermassive black hole binaries (SMBHBs), detectable as a background due to a large number of binaries, or as continuous or burst emission from individual sources. No GW signal has yet been detected, but stringent constraints are already being placed on galaxy evolution models. The SKA will bring this research to fruition. In this chapter, we describe how timing observations using SKA1 will contribute to detecting GWs, or can confirm a detection if a first signal already has been identified when SKA1 commences observations. We describe how SKA observations will identify the source(s) of a GW signal, search for anisotropies in the background, improve models of galaxy evolution, test theories of gravity, and characterise the early inspiral phase of a SMBHB system. We describe the impact of the large number of millisecond pulsars to be discovered by the SKA; and the observing cadence, observation durations, and instrumentation required to reach the necessary sensitivity. We describe the noise processes that will influence the achievable precision with the SKA. We assume a long-term timing programme using the SKA1-MID array and consider the implications of modifications to the current design. We describe the possible benefits from observations using SKA1-LOW. Finally, we describe GW detection prospects with SKA1 and SKA2, and end with a description of the expectations of GW astronomy.
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
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