Related papers: Joint inference for gravitational wave signals and…
We investigate the potential of neural-network based classifiers for discriminating gravitational wave bursts (GWBs) of a given canonical family (e.g. core-collapse supernova waveforms) from typical transient instrumental artifacts…
When searching for gravitational waves in the data from ground-based gravitational wave detectors it is common to use a detection threshold to reduce the number of background events which are unlikely to be the signals of interest. However,…
The two interferometric LIGO gravitational-wave observatories provide the most sensitive data to date to study the gravitational-wave Universe. As part of a global network, they have just completed their third observing run in which they…
The detection of galactic binaries as sources of gravitational waves promises an unprecedented wealth of information about these systems, but also raises several challenges in signal processing. In particular the large number of expected…
A typical approach to developing an analysis algorithm for analyzing gravitational wave data is to assume a particular waveform and use its characteristics to formulate a detection criteria. Once a detection has been made, the algorithm…
Data from ground-based gravitational-wave detectors contains numerous short-duration instrumental artifacts, called "glitches." The high rate of these artifacts in turn results in a significant fraction of gravitational-wave signals from…
We investigate the class of quadratic detectors (i.e., the statistic is a bilinear function of the data) for the detection of poorly modeled gravitational transients of short duration. We point out that all such detection methods are…
The phenomenon of Gravitational Wave (GW) analysis has grown in popularity as technology has advanced and the process of observing gravitational waves has become more precise. Although the sensitivity and the frequency of observation of GW…
Gravitational wave detection requires an in-depth understanding of the physical properties of gravitational wave signals, and the noise from which they are extracted. Understanding the statistical properties of noise is a complex endeavor,…
(abridged for arXiv) With the first direct detection of gravitational waves, the Advanced Laser Interferometer Gravitational-wave Observatory (LIGO) has initiated a new field of astronomy by providing an alternate means of sensing the…
It has become increasingly useful to answer questions in gravitational-wave astronomy using transdimensional models where the number of free parameters can be varied depending on the complexity required to fit the data. Given the growing…
Unlike ground-based gravitational wave detectors, space-based gravitational wave detectors can detect the ringdown signals from massive black hole mergers with large signal-to-noise ratios, help to localize sources and extract their…
Due to its speed after training, machine learning is often envisaged as a solution to a manifold of the issues faced in gravitational-wave astronomy. Demonstrations have been given for various applications in gravitational-wave data…
This paper presents a novel Bayesian strategy for the estimation of smooth signals corrupted by Gaussian noise. The method assumes a smooth evolution of a succession of continuous signals that can have a numerical or an analytical…
We present a method of searching for, and parameterizing, signals from known radio pulsars in data from interferometric gravitational wave detectors. This method has been applied to data from the LIGO and GEO 600 detectors to set upper…
The detection rate for compact binary mergers has grown as the sensitivity of the global network of ground based gravitational wave detectors has improved, now reaching the stage where robust automation of the analyses is essential.…
Transient noise glitches in gravitational-wave detector data limit the sensitivity of searches and contaminate detected signals. In this Paper, we show how glitches can be simulated using generative adversarial networks. We produce hundreds…
We present a machine learning approach using normalising flows for inferring cosmological parameters from gravitational wave events. Our methodology is general to any type of compact binary coalescence event and cosmological model and…
The study of compact binary in-spirals and mergers with gravitational wave observatories amounts to optimizing a theoretical description of the data to best reproduce the true detector output. While most of the research effort in…
Fast, highly accurate, and reliable inference of the sky origin of gravitational waves would enable real-time multi-messenger astronomy. Current Bayesian inference methodologies, although highly accurate and reliable, are slow. Deep…