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Register a new userX-Pipeline: An analysis package for autonomous gravitational-wave burst searches
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Autonomous gravitational-wave searches -- fully automated analyses of data that run without human intervention or assistance -- are desirable for a number of reasons. They are necessary for the rapid identification of gravitational-wave burst candidates, which in turn will allow for follow-up observations by other observatories and the maximum exploitation of their scientific potential. A fully automated analysis would also circumvent the traditional by hand setup and tuning of burst searches that is both labourious and time consuming. We demonstrate a fully automated search with X-Pipeline, a software package for the coherent analysis of data from networks of interferometers for detecting bursts associated with GRBs and other astrophysical triggers. We discuss the methods X-Pipeline uses for automated running, including background estimation, efficiency studies, unbiased optimal tuning of search thresholds, and prediction of upper limits. These are all done automatically via Monte Carlo with multiple independent data samples, and without requiring human intervention. As a demonstration of the power of this approach, we apply X-Pipeline to LIGO data to search for gravitational-wave emission associated with GRB 031108. We find that X-Pipeline is sensitive to signals approximately a factor of 2 weaker in amplitude than those detectable by the cross-correlation technique used in LIGO searches to date. We conclude with the prospects for running X-Pipeline as a fully autonomous, near real-time triggered burst search in the next LSC-Virgo Science Run.
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Searches for gravitational wave bursts that are triggered by the observation of astronomical events require a different mode of analysis than all-sky, blind searches. For one, much more prior information is usually available in a triggered search which can and should be used in the analysis. Second, since the data volume is usually small in a triggered search, it is also possible to use computationally more expensive algorithms for tasks such as data pre-processing that can consume significant computing resources in a high data-volume un-triggered search. From the statistical point of view, the reduction in the parameter space search volume leads to higher sensitivity than an un-triggered search. We describe here a data analysis pipeline for triggered searches, called {tt RIDGE}, and present preliminary results for simulated noise and signals.
coherent WaveBurst (cWB) is a highly configurable pipeline designed to detect a broad range of gravitational-wave (GW) transients in the data of the worldwide network of GW detectors. The algorithmic core of cWB is a time-frequency analysis with the Wilson-Daubechies-Meyer wavelets aimed at the identification of GW events without prior knowledge of the signal waveform. cWB has been in active development since 2003 and it has been used to analyze all scientific data collected by the LIGO-Virgo detectors ever since. On September 14, 2015, the cWB low-latency search detected the first gravitational-wave event, GW150914, a merger of two black holes. In 2019, a public open-source version of cWB has been released with GPLv3 license.
Gravitational waves in the sensitivity band of ground-based detectors can be emitted by a number of astrophysical sources, including not only binary coalescences, but also individual spinning neutron stars. The most promising signals from such sources, although not yet detected, are long-lasting, quasi-monochromatic Continuous Waves (CWs). The PyFstat package provides tools to perform a range of CW data-analysis tasks. It revolves around the F-statistic, a matched-filter detection statistic for CW signals that has been one of the standard methods for LIGO-Virgo CW searches for two decades. PyFstat is built on top of established routines in LALSuite but through its more modern Python interface it enables a flexible approach to designing new search strategies. Hence, it serves a dual function of (i) making LALSuite CW functionality more easily accessible through a Python interface, thus facilitating the new user experience and, for developers, the exploratory implementation of novel methods; and (ii) providing a set of production-ready search classes for use cases not yet covered by LALSuite itself, most notably for MCMC-based followup of promising candidates from wide-parameter-space searches.
The search procedure for burst gravitational waves has been studied using 24 hours of simulated data in a network of three interferometers (Hanford 4-km, Livingston 4-km and Virgo 3-km are the example interferometers). Several methods to detect burst events developed in the LIGO Scientific Collaboration (LSC) and Virgo collaboration have been studied and compared. We have performed coincidence analysis of the triggers obtained in the different interferometers with and without simulated signals added to the data. The benefits of having multiple interferometers of similar sensitivity are demonstrated by comparing the detection performance of the joint coincidence analysis with LSC and Virgo only burst searches. Adding Virgo to the LIGO detector network can increase by 50% the detection efficiency for this search. Another advantage of a joint LIGO-Virgo network is the ability to reconstruct the source sky position. The reconstruction accuracy depends on the timing measurement accuracy of the events in each interferometer, and is displayed in this paper with a fixed source position example.
The global gravitational-wave detector network achieves higher detection rates, better parameter estimates, and more accurate sky localisation, as the number of detectors, $mathcal{I}$ increases. This paper quantifies network performance as a function of $mathcal{I}$ for BayesWave, a source-agnostic, wavelet-based, Bayesian algorithm which distinguishes between true astrophysical signals and instrumental glitches. Detection confidence is quantified using the signal-to-glitch Bayes factor, $mathcal{B}_{mathcal{S},mathcal{G}}$. An analytic scaling is derived for $mathcal{B}_{mathcal{S},mathcal{G}}$ versus $mathcal{I}$, the number of wavelets, and the network signal-to-noise ratio, SNR$_text{net}$, which is confirmed empirically via injections into detector noise of the Hanford-Livingston (HL), Hanford-Livingston-Virgo (HLV), and Hanford-Livingston-KAGRA-Virgo (HLKV) networks at projected sensitivities for the fourth observing run (O4). The empirical and analytic scalings are consistent; $mathcal{B}_{mathcal{S},mathcal{G}}$ increases with $mathcal{I}$. The accuracy of waveform reconstruction is quantified using the overlap between injected and recovered waveform, $mathcal{O}_text{net}$. The HLV and HLKV network recovers $87%$ and $86%$ of the injected waveforms with $mathcal{O}_text{net}>0.8$ respectively, compared to $81%$ with the HL network. The accuracy of BayesWave sky localisation is $approx 10$ times better for the HLV network than the HL network, as measured by the search area, $mathcal{A}$, and the sky areas contained within $50%$ and $90%$ confidence intervals. Marginal improvement in sky localisation is also observed with the addition of KAGRA.
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