Inspirals and mergers of black hole (BHs) and/or neutron star (NSs) binaries are expected to be abundant sources for ground-based gravitational-wave (GW) detectors. We assess the capabilities of Advanced LIGO and Virgo to measure component masses using inspiral waveform models including spin-precession effects using a large ensemble of GW sources {bf randomly oriented and distributed uniformly in volume. For 1000 sources this yields signal-to-noise ratios between 7 and 200}. We make quantitative predictions for how well LIGO and Virgo will distinguish between BHs and NSs and appraise the prospect of using LIGO/Virgo observations to definitively confirm, or reject, the existence of a putative mass gap between NSs ($mleq3 M_odot$) and BHs ($mgeq 5 M_odot$). We find sources with the smaller mass component satisfying $m_2 lesssim1.5 M_odot$ to be unambiguously identified as containing at least one NS, while systems with $m_2gtrsim6 M_odot$ will be confirmed binary BHs. Binary BHs with $m_2<5 M_odot$ (i.e., in the gap) cannot generically be distinguished from NSBH binaries. High-mass NSs ($2<m<3$ $M_odot$) are often consistent with low-mass BH ($m<5 M_odot$), posing a challenge for determining the maximum NS mass from LIGO/Virgo observations alone. Individual sources will seldom be measured well enough to confirm objects in the mass gap and statistical inferences drawn from the detected population will be strongly dependent on the underlying distribution. If nature happens to provide a mass distribution with the populations relatively cleanly separated in chirp mass space, as some population synthesis models suggest, then NSs and BHs are more easily distinguishable.
Gravitational wave data from ground-based detectors is dominated by instrument noise. Signals will be comparatively weak, and our understanding of the noise will influence detection confidence and signal characterization. Mis-modeled noise can produce large systematic biases in both model selection and parameter estimation. Here we introduce a multi-component, variable dimension, parameterized model to describe the Gaussian-noise power spectrum for data from ground-based gravitational wave interferometers. Called BayesLine, the algorithm models the noise power spectral density using cubic splines for smoothly varying broad-band noise and Lorentzians for narrow-band line features in the spectrum. We describe the algorithm and demonstrate its performance on data from the fifth and sixth LIGO science runs. Once fully integrated into LIGO/Virgo data analysis software, BayesLine will produce accurate spectral estimation and provide a means for marginalizing inferences drawn from the data over all plausible noise spectra.
The study of compact binary in-spirals and mergers with gravitational wave observatories amounts to optimizing a theoretical description of the data to best reproduce the true detector output. While most of the research effort in gravitational wave data modeling focuses on the gravitational wave- forms themselves, here we will begin to improve our model of the instrument noise by introducing parameters which allow us to determine the background instrumental power spectrum while simul- taneously characterizing the astrophysical signal. We use data from the fifth LIGO science run and simulated gravitational wave signals to demonstrate how the introduction of noise parameters results in resilience of the signal characterization to variations in an initial estimation of the noise power spectral density. We find substantial improvement in the consistency of Bayes factor calculations when we are able to marginalize over uncertainty in the instrument noise level.
