  We present a parameter estimation framework for gravitational wave (GW)
signals that brings together several ideas to accelerate the inference process.
First, we use the relative binning algorithm to evaluate the
signal-to-noise-ratio timeseries in each detector for a given choice of
intrinsic parameters. Second, we decouple the estimation of the intrinsic
parameters (such as masses and spins of the components) from that of the
extrinsic parameters (such as distance, orientation, and sky location) that
describe a binary compact object coalescence. We achieve this by
semi-analytically marginalizing the posterior distribution over extrinsic
parameters without repeatedly evaluating the waveform for a fixed set of
intrinsic parameters. Finally, we augment samples of intrinsic parameters with
extrinsic parameters drawn from their appropriate conditional distributions. We
implement the method for binaries with aligned spins, restricted to the
quadrupole mode of the signal. Using simulated GW signals, we demonstrate that
the method produces full eleven-dimensional posteriors that match those from
standard Bayesian inference. Our framework takes only ~200 seconds to analyze a
typical binary-black-hole signal and ~250 seconds to analyze a typical
binary-neutron-star signal using one computing core. Such real-time and
accurate estimation of the binary source properties will greatly aid the
interpretation of triggers from gravitational wave searches, as well as the
search for possible electromagnetic counterparts. We make the framework
publicly available via the GW inference package cogwheel.
