Searches for gravitational waves from perturbed black holes in data from LIGO detectors

Black hole perturbation theory predicts that a perturbed black hole will emit gravitational waves in a superposition of quasi-normal modes. Various astrophysical processes can produce such a black hole including the merger of two compact binary neutron stars or black holes. The final form of the waveform from such a system is known as a ringdown. We discuss the search through data from LIGO\u27s fifth science run for ringdown gravitational waves from intermediate mass black holes using a matched filtering pipeline. We outline the improvements to the pipeline since LIGO\u27s fourth science run including the creation of a fully automated post-processing pipeline for coincident triggers, updated waveform simulation code, a new 3D coincidence test to check simultaneously for coincidence in frequency, quality factor, and time, and the use of a new detection statistic through a multi-variate statistical classifier. Results from four months of S5 data have been obtained so far and no gravitational wave candidates were found. The results of the search are ongoing. We demonstrate the improvement in the upper limit on the rate of black hole ringdowns in the local universe over the upper limit of the fourth science run. We investigate parameter recovery of full inspiral-merger-ringdown waveforms using a ringdown matched filter. Finally, we explore the Reduced Basis approach which provides very compact and high-accuracy representations of multi-mode ringdown gravitational waves

Gravitational-wave template banks for novel compact binaries

We introduce a novel method to generate a bank of gravitational-waveform templates of binary black hole (BBH) mergers for matched-filter searches in LIGO, Virgo and Kagra data.We derive a novel expression for the metric approximation to the distance between templates, which is suitable for precessing BBHs and/or systems with higher-order modes (HM) imprints and we use it to meaningfully define a template probability density across the parameter space. We employ a masked autoregressive normalizing flow model which can be conveniently trained to quickly reproduce the target probability distribution and sample templates from it. Thanks to the normalizing flow, our code takes a few {\it hours} to produce random template banks with millions of templates, making it particularly suitable for high-dimensional spaces, such as those associated to precession, eccentricity and/or HM. After validating the performance of our method, we generate a bank for precessing black holes and a bank for aligned-spin binaries with HMs: with only 5% of the injections with fitting factor below the target of 0.97, we show that both banks cover satisfactorily the space. Our publicly released code mbank will enable searches of high-dimensional regions of BBH signal space, hitherto unfeasible due to the prohibitive cost of bank generation

Gravitational-Wave Searches for Cosmic String Cusps in Einstein Telescope Data using Deep Learning

Gravitational-wave searches for cosmic strings are currently hindered by the presence of detector glitches, some classes of which strongly resemble cosmic string signals. This confusion greatly reduces the efficiency of searches. A deep-learning model is proposed for the task of distinguishing between gravitational wave signals from cosmic string cusps and simulated blip glitches in design sensitivity data from the future Einstein Telescope. The model is an ensemble consisting of three convolutional neural networks, achieving an accuracy of 79%, a true positive rate of 76%, and a false positive rate of 18%. This marks the first time convolutional neural networks have been trained on a realistic population of Einstein Telescope glitches. On a dataset consisting of signals and glitches, the model is shown to outperform matched filtering, specifically being better at rejecting glitches. The behaviour of the model is interpreted through the application of several methods, including a novel technique called waveform surgery, used to quantify the importance of waveform sections to a classification model. In addition, a method to visualise convolutional neural network activations for one-dimensional time series is proposed and used. These analyses help further the understanding of the morphological differences between cosmic string cusp signals and blip glitches. Because of its classification speed in the order of magnitude of milliseconds, the deep-learning model is suitable for future use as part of a real-time detection pipeline. The deep-learning model is transverse and can therefore potentially be applied to other transient searches.Comment: 15 pages, 20 figure

Prostitution And Trafficking For Sexual Exploitation: A Case Study Of The Republic Of South Africa And The Kingdom Of The Netherlands

This thesis examines the issues of sex trafficking in South Africa and the Netherlands through an investigation of the relevant policies and laws on sex trafficking and prostitution in the context of existing international human rights instruments. It also addresses the evolution of human rights policy in the context of trafficking for sexual exploitation, analyzing the crime through a feminist exchange theory lens and Wallerstein’s (2013) world system analysis. This research centers on the relationship between international law and national law, concentrating on the socio-political forces and the enforcement of those laws. Policy effectiveness is measured through quantitative data on sexual exploitation provided by the United Nations Office of Drugs and Crime (2018) and opinion data provided by the European Values Study (2019) and the World Values Survey (2014)

Sensitivity Comparison of Searches for Binary Black Hole Coalescences with Ground-based Gravitational-Wave Detectors

Searches for gravitational-wave transients from binary black hole coalescences typically rely on one of two approaches: matched filtering with templates and morphology-independent excess power searches. Multiple algorithmic implementations in the analysis of data from the first generation of ground-based gravitational wave interferometers have used different strategies for the suppression of non-Gaussian noise transients, and targeted different regions of the binary black hole parameter space. In this paper we compare the sensitivity of three such algorithms: matched filtering with full coalescence templates, matched filtering with ringdown templates and a morphology-independent excess power search. The comparison is performed at a fixed false alarm rate and relies on Monte-carlo simulations of binary black hole coalescences for spinning, non-precessing systems with total mass 25-350 solar mass, which covers the parameter space of stellar mass and intermediate mass black hole binaries. We find that in the mass range of 25 -100 solar mass the sensitive distance of the search, marginalized over source parameters, is best with matched filtering to full waveform templates, to within 10 percent at a false alarm rate of 3 events per year. In the mass range of 100-350 solar mass, the same comparison favors the morphology-independent excess power search to within 20 percent. The dependence on mass and spin is also explored.Comment: 11 pages, 2 tables, 25 figure

Multivariate classification with random forests for gravitational wave searches of black hole binary coalescence

Searches for gravitational waves produced by coalescing black hole binaries with total masses ≳25  M_⊙ use matched filtering with templates of short duration. Non-Gaussian noise bursts in gravitational wave detector data can mimic short signals and limit the sensitivity of these searches. Previous searches have relied on empirically designed statistics incorporating signal-to-noise ratio and signal-based vetoes to separate gravitational wave candidates from noise candidates. We report on sensitivity improvements achieved using a multivariate candidate ranking statistic derived from a supervised machine learning algorithm. We apply the random forest of bagged decision trees technique to two separate searches in the high mass (≳25  M_⊙) parameter space. For a search which is sensitive to gravitational waves from the inspiral, merger, and ringdown of binary black holes with total mass between 25  M_⊙ and 100  M_⊙, we find sensitive volume improvements as high as 70_(±13)%–109_(±11)% when compared to the previously used ranking statistic. For a ringdown-only search which is sensitive to gravitational waves from the resultant perturbed intermediate mass black hole with mass roughly between 10  M_⊙ and 600  M_⊙, we find sensitive volume improvements as high as 61_(±4)%–241_(±12)% when compared to the previously used ranking statistic. We also report how sensitivity improvements can differ depending on mass regime, mass ratio, and available data quality information. Finally, we describe the techniques used to tune and train the random forest classifier that can be generalized to its use in other searches for gravitational waves

Fast sky localization of gravitational waves using deep learning seeded importance sampling

Fast, highly accurate, and reliable inference of the sky origin of gravitational waves would enable real-time multimessenger astronomy. Current Bayesian inference methodologies, although highly accurate and reliable, are slow. Deep learning models have shown themselves to be accurate and extremely fast for inference tasks on gravitational waves, but their output is inherently questionable due to the blackbox nature of neural networks. In this work, we merge Bayesian inference and deep learning by applying importance sampling on an approximate posterior generated by a multiheaded convolutional neural network. The neural network parametrizes Von Mises-Fisher and Gaussian distributions for the sky coordinates and two masses for given simulated gravitational wave injections in the LIGO and Virgo detectors. We generate skymaps for unseen gravitational-wave events that highly resemble predictions generated using Bayesian inference in a few minutes. Furthermore, we can detect poor predictions from the neural network, and quickly flag them