Causality and the generalized laws of black hole thermodynamics imply a bound, known as the textit{Bekenstein--Hod universal bound}, on the information emission rate of a perturbed system. Using a time-domain ringdown analysis, we investigate whether
remnant black holes produced by the coalescences observed by Advanced LIGO and Advanced Virgo obey this bound. We find that the bound is verified by the astrophysical black hole population with $94%$ probability, providing a first confirmation of the Bekenstein--Hod bound from black hole systems.
We present a novel method for sampling iso-likelihood contours in nested sampling using a type of machine learning algorithm known as normalising flows and incorporate it into our sampler nessai. Nessai is designed for problems where computing the li
kelihood is computationally expensive and therefore the cost of training a normalising flow is offset by the overall reduction in the number of likelihood evaluations. We validate our sampler on 128 simulated gravitational wave signals from compact binary coalescence and show that it produces unbiased estimates of the system parameters. Subsequently, we compare our results to those obtained with dynesty and find good agreement between the computed log-evidences whilst requiring 2.07 times fewer likelihood evaluations. We also highlight how the likelihood evaluation can be parallelised in nessai without any modifications to the algorithm. Finally, we outline diagnostics included in nessai and how these can be used to tune the samplers settings.
We present a thorough observational investigation of the heuristic quantised ringdown model presented in Foit & Kleban (2019). This model is based on the Bekenstein-Mukhanov conjecture, stating that the area of a black hole horizon is an integer mult
iple of the Planck area $l_P^2$ multiplied by a phenomenological constant, $alpha$, which can be viewed as an additional black hole intrinsic parameter. Our approach is based on a time-domain analysis of the gravitational wave signals produced by the ringdown phase of binary black hole mergers detected by the LIGO and Virgo collaboration. Employing a full Bayesian formalism and taking into account the complete correlation structure among the black hole parameters, we show that the value of $alpha$ cannot be constrained using only GW150914, in contrast to what was suggested in Foit & Kleban (2019). We proceed to repeat the same analysis on the new gravitational wave events detected by the LIGO and Virgo Collaboration up to 1 October 2019, obtaining a combined-event measure equal to $alpha = 15.6^{+20.5}_{-13.3}$ and a combined log odds ratio of $0.1 pm 0.6$, implying that current data are not informative enough to favour or discard this model against general relativity. We then show that using a population of $mathcal{O}(20)$ GW150914-like simulated events - detected by the current infrastructure of ground-based detectors at their design sensitivity - it is possible to confidently falsify the quantised model or prove its validity, in which case probing $alpha$ at the few % level. Finally we classify the stealth biases that may show up in a population study.
The Advanced LIGO and Advanced Virgo gravitational wave detectors have detected a population of binary black hole mergers in their first two observing runs. For each of these events we have been able to associate a potential sky location region repre
sented as a probability distribution on the sky. Thus, at this point we may begin to ask the question of whether this distribution agrees with the isotropic model of the Universe, or if there is any evidence of anisotropy. We perform Bayesian model selection between an isotropic and a simple anisotropic model, taking into account the anisotropic selection function caused by the underlying antenna patterns and sensitivity of the interferometers over the sidereal day. We find an inconclusive Bayes factor of $1.3:1$, suggesting that the data from the first two observing runs is insufficient to pick a preferred model. However, the first detections were mostly poorly localised in the sky (before the Advanced Virgo joined the network), spanning large portions of the sky and hampering detection of potential anisotropy. It will be appropriate to repeat this analysis with events from the recent third LIGO observational run and a more sophisticated cosmological model.
A structured gamma-ray burst jet could explain the dimness of the prompt emission observed from GRB$,170817$A but the exact form of this structure is still ambiguous. However, with the promise of future joint gravitational wave and gamma-ray burst ob
servations, we shall be able to examine populations of binary neutron star mergers rather than a case-by-case basis. We present an analysis that considers gravitational wave triggered binary neutron star events both with and without short gamma-ray burst counterparts assuming that events without a counterpart were observed off-axis. This allows for Bayes factors to be calculated to compare different jet structure models. We perform model comparison between a Gaussian and power-law apparent jet structure on simulated data to demonstrate that the correct model can be distinguished with a log Bayes factor of $>5$ after less than 100 events. Constraints on the apparent structure jet model parameters are also made. After 25(100) events the angular width of the core of a power-law jet structure can be constrained within a $90%$ credible interval of width $ sim9.1(4.4)^{circ} $, and the outer beaming angle to be within $sim19.9(8.5)^{circ}$. Similarly we show the width of a Gaussian jet structure to be constrained to $sim2.8(1.6)^{circ}$.
The detection of the least damped quasi-normal mode from the remnant of the gravitational wave event GW150914 realised the long sought possibility to observationally study the properties of quasi-stationary black hole spacetimes through gravitational
waves. Past literature has extensively explored this possibility and the emerging field has been named black hole spectroscopy. In this study, we present results regarding the ringdown spectrum of GW150914, obtained by application of Bayesian inference to identify and characterise the ringdown modes. We employ a pure time-domain analysis method which infers from the data the time of transition between the non-linear and quasi-linear regime of the post-merger emission in concert with all other parameters characterising the source. We find that the data provides no evidence for the presence of more than one quasi-normal mode. However, from the central frequency and damping time posteriors alone, no unambiguous identification of a single mode is possible. More in-depth analysis adopting a ringdown model based on results in perturbation theory over the Kerr metric, confirms that the data do not provide enough evidence to discriminate among an $l=2$ and the $l=3$ subset of modes. Our work provides the first comprehensive agnostic framework to observationally investigate astrophysical black holes ringdown spectra.
Motivated by the preponderance of so-called heavy black holes in the binary black hole (BBH) gravitational wave (GW) detections to date, and the role that gravitational lensing continues to play in discovering new galaxy populations, we explore the p
ossibility that the GWs are strongly-lensed by massive galaxy clusters. For example, if one of the GW sources were actually located at $z=1$, then the rest-frame mass of the associated BHs would be reduced by a factor $sim2$. Based on the known populations of BBH GW sources and strong-lensing clusters, we estimate a conservative lower limit on the number of BBH mergers detected per detector year at LIGO/Virgos current sensitivity that are multiply-imaged, of $R_{rm detect}simeq10^{-5}{rm yr}^{-1}$. This is equivalent to rejecting the hypothesis that one of the BBH GWs detected to date was multiply-imaged at $<sim4sigma$. It is therefore unlikely but not impossible that one of the GWs is multiply-imaged. We identify three spectroscopically confirmed strong-lensing clusters with well constrained mass models within the $90%$ credible sky localisations of the BBH GWs from LIGOs first observing run. In the event that one of these clusters multiply-imaged one of the BBH GWs, we predict that $20-60%$ of the putative next appearances of the GWs would be detectable by LIGO, and that they would arrive at Earth within three years of first detection.
This document describes a code to perform parameter estimation and model selection in targeted searches for continuous gravitational waves from known pulsars using data from ground-based gravitational wave detectors. We describe the general workings
of the code and characterise it on simulated data containing both noise and simulated signals. We also show how it performs compared to a previous MCMC and grid-based approach to signal parameter estimation. Details how to run the code in a variety of cases are provided in Appendix A.
The Advanced LIGO and Advanced Virgo gravitational wave (GW) detectors will begin operation in the coming years, with compact binary coalescence events a likely source for the first detections. The gravitational waveforms emitted directly encode info
rmation about the sources, including the masses and spins of the compact objects. Recovering the physical parameters of the sources from the GW observations is a key analysis task. This work describes the LALInference software library for Bayesian parameter estimation of compact binary signals, which builds on several previous methods to provide a well-tested toolkit which has already been used for several studies. We show that our implementation is able to correctly recover the parameters of compact binary signals from simulated data from the advanced GW detectors. We demonstrate this with a detailed comparison on three compact binary systems: a binary neutron star, a neutron star black hole binary and a binary black hole, where we show a cross-comparison of results obtained using three independent sampling algorithms. These systems were analysed with non-spinning, aligned spin and generic spin configurations respectively, showing that consistent results can be obtained even with the full 15-dimensional parameter space of the generic spin configurations. We also demonstrate statistically that the Bayesian credible intervals we recover correspond to frequentist confidence intervals under correct prior assumptions by analysing a set of 100 signals drawn from the prior. We discuss the computational cost of these algorithms, and describe the general and problem-specific sampling techniques we have used to improve the efficiency of sampling the compact binary coalescence parameter space.
The Laser Interferometer Space Antenna (LISA) defines new demands on data analysis efforts in its all-sky gravitational wave survey, recording simultaneously thousands of galactic compact object binary foreground sources and tens to hundreds of backg
round sources like binary black hole mergers and extreme mass ratio inspirals. We approach this problem with an adaptive and fully automatic Reversible Jump Markov Chain Monte Carlo sampler, able to sample from the joint posterior density function (as established by Bayes theorem) for a given mixture of signals out of the box, handling the total number of signals as an additional unknown parameter beside the unknown parameters of each individual source and the noise floor. We show in examples from the LISA Mock Data Challenge implementing the full response of LISA in its TDI description that this sampler is able to extract monochromatic Double White Dwarf signals out of colored instrumental noise and additional foreground and background noise successfully in a global fitting approach. We introduce 2 examples with fixed number of signals (MCMC sampling), and 1 example with unknown number of signals (RJ-MCMC), the latter further promoting the idea behind an experimental adaptation of the model indicator proposal densities in the main sampling stage. We note that the experienced runtimes and degeneracies in parameter extraction limit the shown examples to the extraction of a low but realistic number of signals.
