Hierarchical search for compact binary coalescences in the Advanced LIGO's first two observing runs
Kanchan Soni, Bhooshan Uday Gadre, Sanjit Mitra, Sanjeev Dhurandhar

TL;DR
This paper presents a hierarchical search pipeline for detecting compact binary coalescences in LIGO data, significantly reducing computational costs while maintaining detection sensitivity, and successfully analyzing the first two observing runs.
Contribution
It introduces the first implementation of a hierarchical CBC search pipeline in PyCBC, enabling efficient analysis of LIGO data with substantial computational savings.
Findings
Recovered all GWTC-1 events with similar significance
Achieved 20-fold reduction in computational cost
Maintained comparable sensitivity to flat search
Abstract
Detection of many compact binary coalescences (CBCs) is one of the primary goals of the present and future ground-based gravitational-wave (GW) detectors. While increasing the detectors' sensitivities will be crucial in achieving this, efficient data analysis strategies can play a vital role. With given computational power in hand, efficient data analysis techniques can expand the size and dimensionality of the parameter space to search for a variety of GW sources. Matched filtering based analyses that depend on modeled signals to produce adequate signal-to-noise ratios for signal detection may miss them if the parameter space is too restrained. Specifically, the CBC search is currently limited to non-precessing binaries only, where the spins of the components are either aligned or anti-aligned to the orbital angular momentum. A hierarchical search for CBCs is thus well motivated. The…
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