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All-sky search for long-duration gravitational wave transients with initial LIGO

B. P. Abbott et al. (The LIGO Scientific Collaboration and the Virgo Collaboration)
Phys. Rev. D 93, 042005 – Published 12 February 2016
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  • INTRODUCTION
  • ASTROPHYSICAL SOURCES OF LONG GW…
  • DATA SELECTION
  • LONG TRANSIENT GW SEARCH PIPELINE
  • SEARCH SENSITIVITY
  • RESULTS
  • CONCLUSIONS
  • ACKNOWLEDGMENTS
  • APPENDICES
    • References

    Abstract

    We present the results of a search for long-duration gravitational wave transients in two sets of data collected by the LIGO Hanford and LIGO Livingston detectors between November 5, 2005 and September 30, 2007, and July 7, 2009 and October 20, 2010, with a total observational time of 283.0 days and 132.9 days, respectively. The search targets gravitational wave transients of duration 10–500 s in a frequency band of 40–1000 Hz, with minimal assumptions about the signal waveform, polarization, source direction, or time of occurrence. All candidate triggers were consistent with the expected background; as a result we set 90% confidence upper limits on the rate of long-duration gravitational wave transients for different types of gravitational wave signals. For signals from black hole accretion disk instabilities, we set upper limits on the source rate density between 3.4×10−5 and 9.4×10−4  Mpc−3 yr−1 at 90% confidence. These are the first results from an all-sky search for unmodeled long-duration transient gravitational waves.

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    • Received 30 November 2015

    DOI:https://doi.org/10.1103/PhysRevD.93.042005

    © 2016 American Physical Society

    Physics Subject Headings (PhySH)

    1. Research Areas
    Gravitational wave detectionGravitational waves
    Gravitation, Cosmology & Astrophysics

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    Vol. 93, Iss. 4 — 15 February 2016

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    Images

    • Figure 1
      Figure 1

      ft-map of ρ (cross-correlation signal-to-noise ratio) using simulated Gaussian data. A simulated GW signal from an accretion disk instability [58, 59] (model waveform ADI-E, see Table 1) with known sky position is added to the data stream and is visible as a bright, narrow-band track. Blurring around the track is due to the usage of adjacent time segments in estimating σ^Y; the estimate of σ^Y in these bins is affected by the presence of the GW signal.

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    • Figure 2
      Figure 2

      The false alarm rate is shown as a function of the trigger SNRΓ, for 100 time shifts of data from the S5 science run. Distributions are shown before and after applying the postprocessing cuts. Here, SNRfrac refers to a postprocessing cut based on how a trigger’s power is distributed in time (described further in Sec. 4c). Also shown is the FAR distribution generated by a Monte Carlo simulation assuming Gaussian detector noise. Recall that this is an energy SNR, rather than an amplitude SNR.

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    • Figure 3
      Figure 3

      The false alarm rate is shown as a function of the trigger signal-to-noise ratio, SNRΓ, for 100 time shifts of data from the S6 science run. See caption of Fig. 2 for all details.

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    • Figure 4
      Figure 4

      Efficiency of the search pipeline at recovering different waveforms as a function of the distance to the source (for ADI waveforms) or the signal strength hrss (all others). All results shown here used data from the S6 science run. The SNRfrac threshold is set at 0.45, and the recovery threshold is set at SNRΓ=27.13. The error bars are computed using binomial statistics. Top left: ADI waveforms. Top right: sinusoidal waveforms. Bottom left: sine-Gaussian waveforms. Bottom right: white noise burst waveforms.

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    • Figure 5
      Figure 5

      FAR distribution of unshifted triggers from S5 (black circles) and S6 (red triangles) as a function of the trigger signal-to-noise ratio, SNRΓ. The distributions are compared to the estimated background distributions for the S5 (solid black) and S6 (dashed red) data sets. We observe a slight deficit of triggers in S6 that remains within one standard deviation of what is expected from the time-shifted triggers. Also shown is the FAR distribution generated by a Monte Carlo simulation assuming Gaussian detector noise (solid cyan).

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    • Figure 6
      Figure 6

      Loudest event statistic upper limits for the 15 simulated GW signals used to test the sensitivity of the search (calculated with Eq. (14)). 1σ uncertainties are included by adjusting the signal amplitudes upward. Top left: ADI waveforms. Top right: sinusoidal waveforms. Bottom left: sine-Gaussian waveforms. Bottom right: white noise burst waveforms.

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    • Figure 7
      Figure 7

      Loudest event statistic upper limits for the four ADI waveforms used to test the sensitivity of the search. Here, the upper limits are plotted in terms of distance rather than hrss.

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