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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…
Gravitational-wave (GW) astronomy has advanced our understanding of compact mergers through instruments like the Laser Interferometer Gravitational-Wave Observatory (LIGO). However, the extreme sensitivity required for these detections…
Interferometric gravitational-wave detectors like LIGO, GEO600 and Virgo record a surplus of information above and beyond possible gravitational-wave events. These auxiliary channels capture information about the state of the detector and…
Central to the gravitational wave detection problem is the challenge of separating features in the data produced by astrophysical sources from features produced by the detector. Matched filtering provides an optimal solution for Gaussian…
In the data obtained by laser interferometric gravitational wave detectors, transient noise with non-stationary and non-Gaussian features occurs at a high rate. This often results in problems such as detector instability and the hiding…
The groundbreaking discoveries of gravitational waves from binary black-hole mergers and, most recently, coalescing neutron stars started a new era of Multi-Messenger Astrophysics and revolutionized our understanding of the Cosmos. Machine…
Coincident observations with gravitational wave (GW) detectors and other astronomical instruments are in the focus of the experiments with the network of LIGO, Virgo and GEO detectors. They will become a necessary part of the future GW…
The LIGO-Virgo-KAGRA (LVK) network of gravitational-wave (GW) detectors have observed many tens of compact binary mergers to date. Transient, non-Gaussian noise excursions, known as "glitches", can impact signal detection in various ways.…
Real-time noise regression algorithms are crucial for maximizing the science outcomes of the LIGO, Virgo, and KAGRA gravitational-wave detectors. This includes improvements in the detectability, source localization and pre-merger…
Multimessenger astronomy incorporating gravitational radiation is a new and exciting field that will potentially provide significant results and exciting challenges in the near future. With advanced interferometric gravitational wave…
Gravitational wave (GW) transient searches rely on signal-noise discriminators to distinguish astrophysical signals from noise artefacts. These discriminators are typically tuned towards expected signal morphologies, which may limit their…
Broadband frequency output of gravitational-wave detectors is a non-stationary and non-Gaussian time series data stream dominated by noise populated by local disturbances and transient artifacts, which evolve on the same timescale as the…
An important goal for LIGO (the Laser Interferometer Gravitational-Wave Observatory) and Virgo is to find periodic sources of gravitational waves. The LIGO and Virgo detectors are sensitive to a variety of noise of non-astrophysical origin,…
In this study, we employ a convolutional neural network to classify gravitational waves originating from core-collapse supernovae. Training is conducted using spectrograms derived from three-dimensional numerical simulations of waveforms,…
We present a novel machine-learning approach to estimate selection effects in gravitational-wave observations. Using techniques similar to those commonly employed in image classification and pattern recognition, we train a series of…
We introduce the use of deep learning ensembles for real-time, gravitational wave detection of spinning binary black hole mergers. This analysis consists of training independent neural networks that simultaneously process strain data from…
Gravitational wave astronomy has set in motion a scientific revolution. To further enhance the science reach of this emergent field, there is a pressing need to increase the depth and speed of the gravitational wave algorithms that have…
In the last few years, machine learning techniques, in particular convolutional neural networks, have been investigated as a method to replace or complement traditional matched filtering techniques that are used to detect the…
We demonstrate the application of a convolutional neural network to the gravitational wave signals from core collapse supernovae. Using simulated time series of gravitational wave detectors, we show that based on the explosion mechanisms, a…
Data from ground-based gravitational wave detectors are often contaminated by non-Gaussian instrumental artifacts or detector noise transients. Unbiased source property estimation relies on the ability to correctly identify and characterize…