Bayesian Inference for Gravitational Waves from Binary Neutron Star Mergers in Third Generation Observatories.

Third generation (3G) gravitational-wave detectors will observe thousands of coalescing neutron star binaries with unprecedented fidelity. Extracting the highest precision science from these signals is expected to be challenging owing to both high signal-to-noise ratios and long-duration signals. We demonstrate that current Bayesian inference paradigms can be extended to the analysis of binary neutron star signals without breaking the computational bank. We construct reduced-order models for ∼90-min-long gravitational-wave signals covering the observing band (5-2048 Hz), speeding up inference by a factor of ∼1.3×10^{4} compared to the calculation times without reduced-order models. The reduced-order models incorporate key physics including the effects of tidal deformability, amplitude modulation due to Earth's rotation, and spin-induced orbital precession. We show how reduced-order modeling can accelerate inference on data containing multiple overlapping gravitational-wave signals, and determine the speedup as a function of the number of overlapping signals. Thus, we conclude that Bayesian inference is computationally tractable for the long-lived, overlapping, high signal-to-noise-ratio events present in 3G observatories.

Measuring the Primordial Gravitational-Wave Background in the Presence of Astrophysical Foregrounds.

Primordial gravitational waves are expected to create a stochastic background encoding information about the early Universe that may not be accessible by other means. However, the primordial background is obscured by an astrophysical foreground consisting of gravitational waves from compact binaries. We demonstrate a Bayesian method for estimating the primordial background in the presence of an astrophysical foreground. Since the background and foreground signal parameters are estimated simultaneously, there is no subtraction step, and therefore we avoid astrophysical contamination of the primordial measurement, sometimes referred to as "residuals." Additionally, since we include the non-Gaussianity of the astrophysical foreground in our model, this method represents the statistically optimal approach to the simultaneous detection of a multicomponent stochastic background.

Detecting Gravitational Wave Memory without Parent Signals.

Gravitational-wave memory manifests as a permanent distortion of an idealized gravitational-wave detector and arises generically from energetic astrophysical events. For example, binary black hole mergers are expected to emit memory bursts a little more than an order of magnitude smaller in strain than the oscillatory parent waves. We introduce the concept of "orphan memory": gravitational-wave memory for which there is no detectable parent signal. In particular, high-frequency gravitational-wave bursts (≳kHz) produce orphan memory in the LIGO/Virgo band. We show that Advanced LIGO measurements can place stringent limits on the existence of high-frequency gravitational waves, effectively increasing the LIGO bandwidth by orders of magnitude. We investigate the prospects for and implications of future searches for orphan memory.

Detecting Gravitational-Wave Memory with LIGO: Implications of GW150914.

It may soon be possible for Advanced LIGO to detect hundreds of binary black hole mergers per year. We show how the accumulation of many such measurements will allow for the detection of gravitational-wave memory: a permanent displacement of spacetime that comes from strong-field, general relativistic effects. We estimate that Advanced LIGO operating at design sensitivity may be able to make a signal-to-noise ratio 3 (5) detection of memory with ∼35 (90) events with masses and distance similar to GW150914. We highlight the importance of incorporating higher-order gravitational-wave modes for parameter estimation of binary black hole mergers, and describe how our methods can also be used to detect higher-order modes themselves before Advanced LIGO reaches design sensitivity.

Detectability of Gravitational Waves from High-Redshift Binaries.

Recent nondetection of gravitational-wave backgrounds from pulsar timing arrays casts further uncertainty on the evolution of supermassive black hole binaries. We study the capabilities of current gravitational-wave observatories to detect individual binaries and demonstrate that, contrary to conventional wisdom, some are, in principle, detectable throughout the Universe. In particular, a binary with rest-frame mass ≳10^{10}M_{⊙} can be detected by current timing arrays at arbitrarily high redshifts. The same claim will apply for less massive binaries with more sensitive future arrays. As a consequence, future searches for nanohertz gravitational waves could be expanded to target evolving high-redshift binaries. We calculate the maximum distance at which binaries can be observed with pulsar timing arrays and other detectors, properly accounting for redshift and using realistic binary waveforms.

Detecting Gravitational-Wave Transients at 5σ: A Hierarchical Approach.

As second-generation gravitational-wave detectors prepare to analyze data at unprecedented sensitivity, there is great interest in searches for unmodeled transients, commonly called bursts. Significant effort has yielded a variety of techniques to identify and characterize such transient signals, and many of these methods have been applied to produce astrophysical results using data from first-generation detectors. However, the computational cost of background estimation remains a challenging problem; it is difficult to claim a 5σ detection with reasonable computational resources without paying for efficiency with reduced sensitivity. We demonstrate a hierarchical approach to gravitational-wave transient detection, focusing on long-lived signals, which can be used to detect transients with significance in excess of 5σ using modest computational resources. In particular, we show how previously developed seedless clustering techniques can be applied to large data sets to identify high-significance candidates without having to trade sensitivity for speed.