In the first three years of gravitational wave (GW) astronomy, more than ten compact binary coalescences (CBCs) have been detected. As the sensitivities and bandwidths of the detectors improve and new detectors join the network, many more sources are expected to be detected. The goal will not only be to find as many sources as possible in the data but to understand the dynamics of the sources much more precisely. Standard searches are currently restricted to a smaller parameter space which assumes aligned spins. Construction of a larger and denser parameter space, and optimizing the resultant increase in false alarms, pose a serious computational challenge. We present here a two-stage hierarchical strategy to search for CBCs in data from a network of detectors and demonstrate the computational advantage in real life scenario by introducing it in the standard PyCBC pipeline with the usual restricted parameter space. With this implementation, we gain an enormous computational speed up, by a factor of $\sim 20$, over the flat search on LIGO’s first observation run (O1) data. The saving in the computational cost will, in turn, may allow us to search for precessing binaries, will provide more options to search for sources of different kinds and help us to support the never ending urge for extracting more science out of the data with limited resources.

• Received 20 July 2018
• Revised 14 January 2019

DOI:https://doi.org/10.1103/PhysRevD.99.124035