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Register a new userFast evaluation of multi-detector consistency for real-time gravitational wave searches
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Gravitational waves searches for compact binary mergers with LIGO and Virgo are presently a two stage process. First, a gravitational wave signal is identified. Then, an exhaustive search over possible signal parameters is performed. It is critical that the identification stage is efficient in order to maximize the number of gravitational wave sources that are identified. Initial identification of gravitational wave signals with LIGO and Virgo happens in real-time which requires that less than one second of computational time must be used for each one second of gravitational wave data collected. In contrast, subsequent parameter estimation may require hundreds of hours of computational time to analyze the same one second of gravitational wave data. The real-time identification requirement necessitates efficient and often approximate methods for signal analysis. We describe one piece of real-time gravitational-wave identification: an efficient method for ascertaining a signals consistency between multiple gravitational wave detectors suitable for real-time gravitational wave searches for compact binary mergers. This technique was used in analyses of Advanced LIGOs second observing run and Advanced Virgos first observing run.
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The field of transient astronomy has seen a revolution with the first gravitational-wave detections and the arrival of multi-messenger observations they enabled. Transformed by the first detection of binary black hole and binary neutron star mergers, computational demands in gravitational-wave astronomy are expected to grow by at least a factor of two over the next five years as the global network of kilometer-scale interferometers are brought to design sensitivity. With the increase in detector sensitivity, real-time delivery of gravitational-wave alerts will become increasingly important as an enabler of multi-messenger followup. In this work, we report a novel implementation and deployment of deep learning inference for real-time gravitational-wave data denoising and astrophysical source identification. This is accomplished using a generic Inference-as-a-Service model that is capable of adapting to the future needs of gravitational-wave data analysis. Our implementation allows seamless incorporation of hardware accelerators and also enables the use of commercial or private (dedicated) as-a-service computing. Based on our results, we propose a paradigm shift in low-latency and offline computing in gravitational-wave astronomy. Such a shift can address key challenges in peak-usage, scalability and reliability, and provide a data analysis platform particularly optimized for deep learning applications. The achieved sub-millisecond scale latency will also be relevant for any machine learning-based real-time control systems that may be invoked in the operation of near-future and next generation ground-based laser interferometers, as well as the front-end collection, distribution and processing of data from such instruments.
We present a new veto procedure to distinguish between continuous gravitational wave (CW) signals and the detector artifacts that can mimic their behavior. The veto procedure exploits the fact that a long-lasting coherent disturbance is less likely than a real signal to exhibit a Doppler modulation of astrophysical origin. Therefore, in the presence of an outlier from a search, we perform a multi-step search around the frequency of the outlier with the Doppler modulation turned off (DM-off), and compare these results with the results from the original (DM-on) search. If the results from the DM-off search are more significant than those from the DM-on search, the outlier is most likely due to an artifact rather than a signal. We tune the veto procedure so that it has a very low false dismissal rate. With this veto, we are able to identify as coherent disturbances >99.9% of the 6349 candidates from the recent all-sky low-frequency Einstein@Home search on the data from the Advanced LIGO O1 observing run [1]. We present the details of each identified disturbance in the Appendix.
In hierarchical searches for continuous gravitational waves, clustering of candidates is an important postprocessing step because it reduces the number of noise candidates that are followed-up at successive stages [1][7][12]. Previous clustering procedures bundled together nearby candidates ascribing them to the same root cause (be it a signal or a disturbance), based on a predefined cluster volume. In this paper, we present a procedure that adapts the cluster volume to the data itself and checks for consistency of such volume with what is expected from a signal. This significantly improves the noise rejection capabilities at fixed detection threshold, and at fixed computing resources for the follow-up stages, this results in an overall more sensitive search. This new procedure was employed in the first Einstein@Home search on data from the first science run of the advanced LIGO detectors (O1) [11].
We study a cross-shaped cavity filled with superfluid $^4$He as a prototype resonant-mass gravitational wave detector. Using a membrane and a re-entrant microwave cavity as a sensitive optomechanical transducer, we were able to observe the thermally excited high-$Q$ acoustic modes of the helium at 20 mK temperature and achieved a strain sensitivity of $8 times 10^{-19}$ Hz$^{-1/2}$ to gravitational waves. To facilitate the broadband detection of continuous gravitational waves, we tune the kilohertz-scale mechanical resonance frequencies up to 173 Hz/bar by pressurizing the helium. With reasonable improvements, this architecture will enable the search for GWs in the 1-30 kHz range, relevant for a number of astrophysical sources both within and beyond the Standard Model.
KAGRA is a new gravitational wave detector which aims to begin joint observation with Advanced LIGO and Advanced Virgo from late 2019. Here, we present KAGRAs possible upgrade plans to improve the sensitivity in the decade ahead. Unlike other state-of-the-art detectors, KAGRA requires different investigations for the upgrade since it is the only detector which employs cryogenic cooling of the test mass mirrors. In this paper, investigations on the upgrade plans which can be realized by changing the input laser power, increasing the mirror mass, and injecting frequency dependent squeezed vacuum are presented. We show how each upgrade affects to the detector frequency bands and also discuss impacts on gravitational-wave science. We then propose an effective progression of upgrades based on technical feasibility and scientific scenarios.
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