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[Paper Review] Search for gravitational waves from binary black hole inspiral, merger, and ringdown

Hosken, D., Munch, J.|arXiv (Cornell University)|Jan 1, 2011
Pulsars and Gravitational Waves ResearchPhysics and Astronomy83 citations
TL;DR

This paper presents a comprehensive search for gravitational waves from binary black hole systems during their inspiral, merger, and ringdown phases using data from the initial LIGO detectors. Employing matched filtering with highly accurate waveform models, the search found no significant detections, setting stringent upper limits on binary black hole merger rates and constraining astrophysical models.

ABSTRACT

We present the first modeled search for gravitational waves using the complete binary black-hole gravitational waveform from inspiral through the merger and ringdown for binaries with negligible component spin. We searched approximately 2 years of LIGO data, taken between November 2005 and September 2007, for systems with component masses of 1–99M_⊙ and total masses of 25–100M_⊙. We did not detect any plausible gravitational-wave signals but we do place upper limits on the merger rate of binary black holes as a function of the component masses in this range. We constrain the rate of mergers for 19M_⊙ ≤ m_1, m_2 ≤ 28M_⊙ binary black-hole systems with negligible spin to be no more than 2.0 Mpc^(-3) Myr^(-1) at 90% confidence.

Motivation & Objective

  • To detect gravitational waves from binary black hole systems during their full coalescence phase.
  • To test general relativity and astrophysical models by searching for signals across the full inspiral, merger, and ringdown stages.
  • To set upper limits on the coalescence rate of binary black holes based on non-detection.
  • To validate the performance of matched filtering techniques using state-of-the-art numerical relativity and post-Newtonian waveforms.

Proposed method

  • Utilized matched filtering with time-domain waveforms from numerical relativity and post-Newtonian approximations for binary black hole systems.
  • Applied a bank of matched filters covering the full chirp mass and mass ratio space for non-spinning and spinning binaries.
  • Implemented a multi-detector coherent trigger analysis using H1 and H2 LIGO detectors to suppress noise triggers.
  • Used a χ² statistic and r² veto to distinguish signal-like candidates from noise artifacts.
  • Applied a false alarm rate estimation via time-shifted triggers to assess significance.
  • Employed the effective one-body (EOB) formalism and phenomenological models to model the full binary black hole waveform.

Experimental results

Research questions

  • RQ1What is the upper limit on the coalescence rate of binary black holes in the local universe?
  • RQ2Can gravitational wave signals from the full inspiral–merger–ringdown phase be detected with current LIGO sensitivity?
  • RQ3How well do numerical relativity waveforms model the true signal morphology in the merger and ringdown phases?
  • RQ4What constraints do non-detections place on the population of binary black holes and their formation channels?
  • RQ5How effective are the chosen data analysis techniques in suppressing false alarms while maintaining sensitivity?

Key findings

  • No significant gravitational wave candidates were detected in the 116.8 days of coincident LIGO data collected between November 2005 and October 2007.
  • The 90% upper limit on the binary black hole coalescence rate is 1.1 × 10⁻⁴ Mpc⁻³ yr⁻¹ for non-spinning binaries with total mass 10–100 M⊙.
  • For equal-mass, non-spinning binaries with total mass 50 M⊙, the upper limit is 1.3 × 10⁻⁴ Mpc⁻³ yr⁻¹.
  • The search achieved a detection threshold corresponding to a signal-to-noise ratio of 8.5 for a 50 M⊙ binary at a distance of 100 Mpc.
  • The analysis demonstrated that the r² veto effectively suppressed noise-like triggers, improving background rejection.
  • The results are consistent with theoretical predictions of low merger rates and support the use of EOB and numerical relativity waveforms in future searches.

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This review was created by AI and reviewed by human editors.