Measuring neutron star tidal deformability with Advanced LIGO: a   Bayesian analysis of neutron star - black hole binary observations
  The discovery of gravitational waves (GW) by Advanced LIGO has ushered us into an era of observational GW astrophysics. Compact binaries remain the primary target sources for LIGO, of which neutron star-black hole (NSBH) binaries form an important subset. GWs from NSBH sources carry signatures of (a) the tidal distortion of the neutron star by its companion black hole during inspiral, and (b) its potential tidal disruption near merger. In this paper, we present a Bayesian study of the measurability of neutron star tidal deformability $\Lambda_\mathrm{NS}\propto (R/M)^{5}$ using observation(s) of inspiral-merger GW signals from disruptive NSBH coalescences, taking into account the crucial effect of black hole spins. First, we find that if non-tidal templates are used to estimate source parameters for an NSBH signal, the bias introduced in the estimation of non-tidal physical parameters will only be significant for loud signals with signal-to-noise ratios $> 30$. For similarly loud signals, we also find that we can begin to put interesting constraints on $\Lambda_\mathrm{NS}$ (factor of 1-2) with individual observations. Next, we study how a population of realistic NSBH detections will improve our measurement of neutron star tidal deformability. For astrophysical populations of $disruptive$ NSBH mergers, we find 20-35 events to be sufficient to constrain $\Lambda_\mathrm{NS}$ within $\pm 25-50\%$, depending on the chosen equation of state. In this we also assume that LIGO will detect black holes with masses within the astrophysical $mass$-$gap$. If the mass-gap remains preserved in NSBHs detected by LIGO, we estimate that $25\%$ $additional$ detections will furnish comparable tidal measurement accuracy. In both cases, we find that the loudest 5-10 events to provide most of the tidal information, thereby facilitating targeted follow-ups of NSBHs in the upcoming LIGO-Virgo runs. 
Prayush Kumar, Michael P\"urrer, Harald P. Pfeiffer
gr-qc