Search for gravitational waves from binary black hole inspirals in LIGO data

TL;DR

This paper reports the first LIGO search for gravitational waves from the inspiral phase of binary black holes with component masses between $3$ and $20\,M_\odot$ using second science run data. The analysis employs phenomenological BCV templates to cover late-inspiral dynamics and partial merger features, maximized via matched filtering across a bank of templates and a tailored coincidence strategy between detectors. No gravitational-wave candidates were identified above the estimated background, leading to a conservative 90% upper limit on BBH coalescence rates of approximately $38$ yr$^{-1}$ MWEG$^{-1}$ under specified population assumptions and waveform mix; Monte Carlo injections indicate good sensitivity at nearby distances, particularly for EOB-like waveforms. The results demonstrate LIGO's growing capability to probe BBH inspirals and guide methodological refinements for future detections as the detectors reach design sensitivity and broader waveform models become available.

Abstract

We report on a search for gravitational waves from binary black hole inspirals in the data from the second science run of the LIGO interferometers. The search focused on binary systems with component masses between 3 and 20 solar masses. Optimally oriented binaries with distances up to 1 Mpc could be detected with efficiency of at least 90%. We found no events that could be identified as gravitational waves in the 385.6 hours of data that we searched.

Figures (8)

• Figure 1: Range (distance at which an optimally oriented inspiraling binary of given total mass would produce a signal-to-noise ratio of $8$) of the LIGO interferometers during S2. The error bars are calculated from the fluctuations of the noise in the LIGO interferometers during S2.
• Figure 2: The algorithm used to divide science segments into data analysis segments. Science segments are divided into $2048$ s blocks overlapped by $128$ s. (Science segments shorter than $2048$ s are ignored.) An additional block with a larger overlap is added to cover any remaining data at the end of a science segment. Each block is divided into $15$ analysis sesgments of length $256$ s for filtering. The first and last $64$ s of each analysis segment are ignored, so the segments overlap by $128$ s. Areas shaded black are searched for triggers by the search pipeline. The gray area in the last block of the science segment is not searched for triggers as this time is covered by the preceding block, although these data points are used in estimating the noise power spectral density for the final block.
• Figure 3: Time domain plots of BCV waveforms for different values of $\alpha_{F}$. The top plot is for $\alpha_F = 0$, the middle is for $\alpha_F = 1$ and the bottom is for $\alpha_F = 2$. For all three waveforms $\psi_0 = 150000 \ \mathrm{Hz}^{5/3}$, $\psi_3 = -1500 \ \mathrm{Hz}^{2/3}$ and $f_{\mathrm{cut}} = 500$ Hz. It can be seen that the behavior is not that of a typical inspiral waveform for $\alpha_F = 2$.
• Figure 4: The LLO and LHO SNRs of the accidental coincidences from the time-shifted triggers (crosses) and the triggers from the simulated signal injections (circles) are shown. The dashed lines show the equal false-alarm contours.
• Figure 5: Expected accidental coincidences per S2 (triangles) with one standard deviation bars. The number of events in the S2 (circles) is overlayed.