Parameter estimation for signals from compact binary inspirals injected
  into LIGO data
  During the fifth science run of the Laser Interferometer Gravitational-wave
Observatory (LIGO), signals modelling the gravitational waves emitted by
coalescing non-spinning compact-object binaries were injected into the LIGO
data stream. We analysed the data segments into which such injections were made
using a Bayesian approach, implemented as a Markov-chain Monte-Carlo technique
in our code SPINspiral. This technique enables us to determine the physical
parameters of such a binary inspiral, including masses and spin, following a
possible detection trigger. For the first time, we publish the results of a
realistic parameter-estimation analysis of waveforms embedded in real detector
noise. We used both spinning and non-spinning waveform templates for the data
analysis and demonstrate that the intrinsic source parameters can be estimated
with an accuracy of better than 1-3% in the chirp mass and 0.02-0.05 (8-20%) in
the symmetric mass ratio if non-spinning waveforms are used. We also find a
bias between the injected and recovered parameters, and attribute it to the
difference in the post-Newtonian orders of the waveforms used for injection and
analysis.
