Related papers: Transient Classification in LIGO data using Differ…
We investigate the computational requirements for all-sky, all-frequency searches for gravitational waves from spinning neutron stars, using archived data from interferometric gravitational wave detectors such as LIGO. These sources are…
In this article, we introduce a methodology based on an analytical model of a damped harmonic oscillator subject to random forcing to generate transient gravitational wave signals. Such a model incorporates a simulated linear high-frequency…
Gravitational-wave data analysis is rapidly absorbing techniques from deep learning, with a focus on convolutional networks and related methods that treat noisy time series as images. We pursue an alternative approach, in which waveforms…
There are many occasions when one does not have complete information in order to classify objects into different classes, and yet it is important to do the best one can since other decisions depend on that. In astronomy, especially…
Gravitational-wave data from advanced-era interferometric detectors consists of background Gaussian noise, frequent transient artefacts, and rare astrophysical signals. Multiple search algorithms exist to detect the signals from compact…
The LIGO detectors collected about 4 months of data in 2003-2004 during two science runs, S2 and S3. Several environmental and auxiliary channels that monitor the instruments' physical environment and overall interferometric operation were…
The fourth science run of the LIGO and GEO 600 gravitational-wave detectors, carried out in early 2005, collected data with significantly lower noise than previous science runs. We report on a search for short-duration gravitational-wave…
Gravitational wave astronomy is a rapidly growing field of modern astrophysics, with observations being made frequently by the LIGO detectors. Gravitational wave signals are often extremely weak and the data from the detectors, such as…
Known for their efficiency in analyzing large data sets, machine learning classifiers are widely used in wide-field sky surveys. The upcoming Vera C. Rubin Observatory Legacy of Time and Space Survey (LSST) will generate millions of alerts…
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.…
The most general searches for gravitational wave transients (GWTs) rely on data analysis methods that do not assume prior knowledge of the signal waveform, direction, or arrival time on Earth. These searches provide data-driven signal…
Existing coherent network analysis techniques for detecting gravitational-wave bursts simultaneously test data from multiple observatories for consistency with the expected properties of the signals. These techniques assume the output of…
With large numbers of transients discovered by current and future imaging surveys, machine learning is increasingly applied to light curve and host galaxy properties to select events for follow-up. However, finding rare types of transients…
Gravitational waves, first predicted by Albert Einstein within the framework of general relativity, were confirmed in 2015 by the LIGO/Virgo collaboration, marking a pivotal breakthrough in astrophysics. Despite this achievement, a key…
Gravitational wave data from ground-based detectors is dominated by instrument noise. Signals will be comparatively weak, and our understanding of the noise will influence detection confidence and signal characterization. Mis-modeled noise…
Data from gravitational wave detectors are recorded as time series that include contributions from myriad noise sources in addition to any gravitational wave signals. When regularly sampled data are available, such as for ground based and…
This work investigates whether large language models (LLMs) offer advantages over traditional neural networks for astronomical data processing, in regimes with non-Gaussian, non-stationary noise and limited labeled samples. Gravitational…
In recent years, much work have studied the use of convolutional neural networks for gravitational-wave detection. However little work pay attention to whether the transient noise can trigger the CNN model or not. In this paper, we study…
The primary challenge in the study of explosive astrophysical transients is their detection and characterisation using multiple messengers. For this purpose, we have developed a new data-driven discovery framework, based on deep learning.…
In 2009-2010, the Laser Interferometer Gravitational-wave Observa- tory (LIGO) operated together with international partners Virgo and GEO600 as a network to search for gravitational waves of astrophysical origin. The sensitiv- ity of these…