463 research outputs found

    The unresolved stochastic background from compact binary mergers detectable by next-generation ground-based gravitational-wave observatories

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    The next generation of ground-based gravitational-wave detectors will look much deeper into the Universe and have unprecedented sensitivities and low-frequency capabilities. Especially alluring is the possibility of detecting an early-Universe cosmological stochastic background that could provide important insights into the beginnings of our Universe and fundamental physics at extremely high energies. However, even if next-generation detectors are sensitive to cosmological stochastic backgrounds, they will be masked by more dominant astrophysical backgrounds, namely the residual background from the imperfect subtraction of resolvable compact binary coalescences (CBCs) as well as the CBC background from individually unresolvable CBCs. Using our latest knowledge of masses, rates, and delay time distributions, we present a data-driven estimate of the unresolvable CBC background that will be seen by next-generation detectors. Accounting for statistical and systematic errors, this estimate quantifies an important piece in the CBC noise budget for next-generation detectors and can help inform detector design and subtraction algorithms. We compare our results with predictions for backgrounds from several cosmological sources in the literature, finding that the unresolvable background will likely be a significant impediment for many models. This motivates the need for simultaneous inference methods or other statistical techniques to detect early-Universe cosmological backgrounds.Comment: 19 pages, 8 figure

    Environmental Noise in Advanced LIGO Detectors

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    The sensitivity of the advanced LIGO detectors to gravitational waves can be affected by environmental disturbances external to the detectors themselves. Since the transition from the former initial LIGO phase, many improvements have been made to the equipment and techniques used to investigate these environmental effects. These methods have aided in tracking down and mitigating noise sources throughout the first three observing runs of the advanced detector era, keeping the ambient contribution of environmental noise below the background noise levels of the detectors. In this paper we describe the methods used and how they have led to the mitigation of noise sources, the role that environmental monitoring has played in the validation of gravitational wave events, and plans for future observing runs

    Unified p astro for gravitational waves: Consistently combining information from multiple search pipelines

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    Recent gravitational-wave transient catalogs have used p astro, the probability that a gravitational-wave candidate is astrophysical, to select interesting candidates for further analysis. Unlike false alarm rates, which exclusively capture the statistics of the instrumental noise triggers, p astro incorporates the rate at which triggers are generated by both astrophysical signals and instrumental noise in estimating the probability that a candidate is astrophysical. Multiple search pipelines can independently calculate p astro, each employing a specific data reduction. While the range of p astro results can help indicate the range of uncertainties in its calculation, it complicates interpretation and subsequent analyses. We develop a statistical formalism to calculate a unified p astro for gravitational-wave candidates, consistently accounting for triggers from all pipelines, thereby incorporating extra information about a signal that is not available with any one single pipeline. We demonstrate the properties of this method using a toy model and by application to the publicly available list of gravitational-wave candidates from the first half of the third LIGO-Virgo-KAGRA observing run. Adopting a unified p astro for future catalogs would provide a simple and easy-to-interpret selection criterion that incorporates a more complete understanding of the strengths of the different search pipelines

    Data quality up to the third observing run of Advanced LIGO: Gravity Spy glitch classifications

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    Understanding the noise in gravitational-wave detectors is central to detecting and interpreting gravitational-wave signals. Glitches are transient, non-Gaussian noise features that can have a range of environmental and instrumental origins. The Gravity Spy project uses a machine-learning algorithm to classify glitches based upon their time–frequency morphology. The resulting set of classified glitches can be used as input to detector-characterisation investigations of how to mitigate glitches, or data-analysis studies of how to ameliorate the impact of glitches. Here we present the results of the Gravity Spy analysis of data up to the end of the third observing run of Advanced LIGO. We classify 233981 glitches from LIGO Hanford and 379805 glitches from LIGO Livingston into morphological classes. We find that the distribution of glitches differs between the two LIGO sites. This highlights the potential need for studies of data quality to be individually tailored to each gravitational-wave observatory
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