We discuss two approaches to searches for gravitational-wave (GW) and electromagnetic (EM) counterparts of binary neutron star mergers. The first approach relies on triggering archival searches of GW detector data based on detections of EM transients. We introduce a quantitative approach to evaluate the improvement to GW detector reach due to the extra information gained from the EM transient and the increased confidence in the presence of a signal from a binary merger. We also advocate utilizing other transients in addition to short gamma ray bursts. The second approach involves following up GW candidates with targeted EM observations. We argue for the use of slower but optimal parameter-estimation techniques to localize the source on the sky, and for a more sophisticated use of astrophysical prior information, including galaxy catalogs, to find preferred followup locations.
One of the goals of gravitational-wave astronomy is simultaneous detection of gravitational-wave signals from merging compact-object binaries and the electromagnetic transients from these mergers. With the next generation of advanced ground-based gravitational wave detectors under construction, we examine the benefits of the proposed extension of the detector network to include a fourth site in Australia in addition to the network of Hanford, Livingston and Cascina sites. Using Bayesian parameter-estimation analyses of simulated gravitational-wave signals from a range of coalescing-binary locations and orientations, we study the improvement in parameter estimation. We find that an Australian detector can break degeneracies in several parameters; in particular, the localization of the source on the sky is improved by a factor of ~4, with more modest improvements in distance and binary inclination estimates. This enhanced ability to localize sources on the sky will be crucial in any search for electromagnetic counterparts to detected gravitational-wave signals.
Selection among alternative theoretical models given an observed data set is an important challenge in many areas of physics and astronomy. Reversible-jump Markov chain Monte Carlo (RJMCMC) is an extremely powerful technique for performing Bayesian model selection, but it suffers from a fundamental difficulty: it requires jumps between model parameter spaces, but cannot efficiently explore both parameter spaces at once. Thus, a naive jump between parameter spaces is unlikely to be accepted in the MCMC algorithm and convergence is correspondingly slow. Here we demonstrate an interpolation technique that uses samples from single-model MCMCs to propose inter-model jumps from an approximation to the single-model posterior of the target parameter space. The interpolation technique, based on a kD-tree data structure, is adaptive and efficient in modest dimensionality. We show that our technique leads to improved convergence over naive jumps in an RJMCMC, and compare it to other proposals in the literature to improve the convergence of RJMCMCs. We also demonstrate the use of the same interpolation technique as a way to construct efficient global proposal distributions for single-model MCMCs without prior knowledge of the structure of the posterior distribution, and discuss improvements that permit the method to be used in higher-dimensional spaces efficiently.
We present an up-to-date, comprehensive summary of the rates for all types of compact binary coalescence sources detectable by the Initial and Advance
Gravitational waves (GWs) from the inspiral of a neutron star (NS) or stellar-mass black hole (BH) into an intermediate-mass black hole (IMBH) with mass between ~50 and ~350 solar masses may be detectable by the planned advanced generation of ground-based GW interferometers. Such intermediate mass ratio inspirals (IMRIs) are most likely to be found in globular clusters. We analyze four possible IMRI formation mechanisms: (1) hardening of an NS-IMBH or BH-IMBH binary via three-body interactions, (2) hardening via Kozai resonance in a hierarchical triple system, (3) direct capture, and (4) inspiral of a compact object from a tidally captured main-sequence star; we also discuss tidal effects when the inspiraling object is an NS. For each mechanism we predict the typical eccentricities of the resulting IMRIs. We find that IMRIs will have largely circularized by the time they enter the sensitivity band of ground-based detectors. Hardening of a binary via three-body interactions, which is likely to be the dominant mechanism for IMRI formation, yields eccentricities under 10^-4 when the GW frequency reaches 10 Hz. Even among IMRIs formed via direct captures, which can have the highest eccentricities, around 90% will circularize to eccentricities under 0.1 before the GW frequency reaches 10 Hz. We estimate the rate of IMRI coalescences in globular clusters and the sensitivity of a network of three Advanced LIGO detectors to the resulting GWs. We show that this detector network may see up to tens of IMRIs per year, although rates of one to a few per year may be more plausible. We also estimate the loss in signal-to-noise ratio that will result from using circular IMRI templates for data analysis and find that, for the eccentricities we expect, this loss is negligible.
Extreme-mass-ratio inspirals (EMRIs) of ~ 1-10 solar-mass compact objects into ~ million solar-mass massive black holes can serve as excellent probes of strong-field general relativity. The Laser Interferometer Space Antenna (LISA) is expected to detect gravitational wave signals from apprxomiately one hundred EMRIs per year, but the data analysis of EMRI signals poses a unique set of challenges due to their long duration and the extensive parameter space of possible signals. One possible approach is to carry out a search for EMRI tracks in the time-frequency domain. We have applied a time-frequency search to the data from the Mock LISA Data Challenge (MLDC) with promising results. Our analysis used the Hierarchical Algorithm for Clusters and Ridges to identify tracks in the time-frequency spectrogram corresponding to EMRI sources. We then estimated the EMRI source parameters from these tracks. In these proceedings, we discuss the results of this analysis of the MLDC round 1.3 data.
