The PyCBC search for gravitational waves from compact binary coalescence (1508.02357v3)

Abstract: We describe the PyCBC search for gravitational waves from compact-object binary coalescences in advanced gravitational-wave detector data. The search was used in the first Advanced LIGO observing run and unambiguously identified two black hole binary mergers, GW150914 and GW151226. At its core, the PyCBC search performs a matched-filter search for binary merger signals using a bank of gravitational-wave template waveforms. We provide a complete description of the search pipeline including the steps used to mitigate the effects of noise transients in the data, identify candidate events and measure their statistical significance. The analysis is able to measure false-alarm rates as low as one per million years, required for confident detection of signals. Using data from initial LIGO's sixth science run, we show that the new analysis reduces the background noise in the search, giving a 30% increase in sensitive volume for binary neutron star systems over previous searches.

• The paper presents the PyCBC pipeline, a matched-filter search method that efficiently constructs a four-dimensional template bank to improve binary coalescence detections.
• The pipeline employs innovative noise transient mitigation through gating and a compute-efficient chi-squared test to reduce false positives.
• The improved pipeline increases sensitivity by up to 30% for binary neutron star detections, enabling more reliable gravitational-wave observations with LIGO.

An Overview of the PyCBC Pipeline for Gravitational Wave Search

The paper presents a detailed examination of the PyCBC search pipeline, a robust tool designed for the identification of gravitational waves originating from compact binary coalescences. This exploration is especially pertinent for data sourced from advanced gravitational wave detectors like Advanced LIGO. Throughout the paper, the authors provide a comprehensive description of the PyCBC pipeline, detailing innovative methodologies designed to enhance sensitivity and reduce computational resources compared to previous pipeline iterations.

Key Elements of the PyCBC Pipeline

A primary focus of the paper is on the design and function of the PyCBC algorithm, which utilizes a matched-filter search to detect binary merger signals against a bank of pre-calculated gravitational-wave template waveforms. Noteworthy advancements within the PyCBC framework include:

1. Template Bank Generation: The pipeline constructs a robust template bank covering a four-dimensional parameter space comprising the mass and spin of compact binaries. The development employs a harmonically averaged power spectral density (PSD) over time and detectors, allowing it to reduce noise effects and computational demands.
2. Noise Transient Mitigation: Through a process termed "gating," the pipeline identifies and eliminates non-stationary noise transients from the data prior to filtering, efficiently reducing the incidence of false positives and enhancing the reliability of signal detection.
3. Signal Consistency Tests: The pipeline introduces a more compute-efficient chi-squared test. This test ascertains the signal consistency across different frequency bands by focusing calculation efforts directly on matched-filter signal-to-noise ratio (SNR) peak times, significantly reducing computational load.
4. Coincidence Test: By implementing an exact match coincidence requirement, the PyCBC pipeline ensures that candidate signals observed across different detectors are derived from the exact same template. The approach emphasizes parameter and time-of-arrival congruence, thus tightening the criteria for genuine signal identification.
5. Background Noise Estimation: The paper discusses enhanced false-alarm rate measurement techniques through sophisticated time-shift analyses, effectively estimating the probability of coincidental noise-induced correlations and assigning statistical significance to potential signals.

Results and Implications

The analysis in the paper reveals that the PyCBC pipeline can achieve up to a 30% increase in sensitivity volume for binary neutron star systems compared to previous methods employed during LIGO's Initial Science run. Even with significant improvements in sensitivity, the computational efficiency remains comparable to previous pipelines, thereby optimizing resource usage—essential for the scalability of such extensive analytical tasks.

These enhancements have direct implications for the field of gravitational-wave astronomy. The increase in pipeline sensitivity not only boosts the likelihood of detecting gravitational wave events—critical for verifying our understanding of astrophysical phenomena—but also facilitates the expansion of multi-messenger astronomy approaches involving gravitational wave sources associated with electromagnetic counterparts.

Future Developments

The adaptability of the PyCBC framework is emphasized as a foundation for ongoing and future advancements. Proposals include broadening the mass-space range, incorporating varied template waveform families, and improving coherent search methods across global detector networks. These refinements could further bolster the discovery potential of the PyCBC search pipeline, supporting the broader scientific goals of gravitational wave astrophysics.

In summation, the PyCBC pipeline presents substantial improvements in the search for gravitational waves, as evidenced through both theoretical enhancements and empirical results from historical datasets. As gravitational wave science progresses, developments such as those presented in the paper will be instrumental in expanding our catalog of astrophysical events, inching us closer to a comprehensive understanding of the dynamic universe.