相关论文: Vision Transformer for Transient Noise Classificat…
The LIGO Scientific Collaboration and the Virgo Collaboration have cataloged eleven confidently detected gravitational-wave events during the first two observing runs of the advanced detector era. All eleven events were consistent with…
We present a new method which accounts for changes in the properties of gravitational-wave detector noise over time in the PyCBC search for gravitational waves from compact binary coalescences. We use information from LIGO data quality…
The gravitational wave detectors used by the LIGO Scientific Collaboration, and the Virgo Collaboration are incredibly sensitive instruments which frequently detect non-stationary, non-Gaussian noise transients. iDQ is a statistical…
This work investigates the temporal distribution of glitches detected by LIGO, focusing on the morphological classification provided by the Gravity Spy project. Starting from the hypothesis that these events follow a Poisson process, we…
The field of gravitational wave (GW) detection is progressing rapidly, with several next-generation observatories on the horizon, including LISA. GW data is challenging to analyze due to highly variable signals shaped by source properties…
The proposed Laser Interferometer Space Antenna (LISA) mission is tasked with the detection and characterization of gravitational waves from various sources in the universe. This endeavor is challenged by transient displacement and…
The detection of gravitational waves is considered to be one of the most magnificent discoveries of the century. Due to the high computational cost of matched filtering pipeline, there is a hunt for an alternative powerful system. I…
We demonstrate unprecedented accuracy for rapid gravitational-wave parameter estimation with deep learning. Using neural networks as surrogates for Bayesian posterior distributions, we analyze eight gravitational-wave events from the first…
Wide-parameter-space searches for continuous gravitational waves using semi-coherent matched-filter methods require enormous computing power, which limits their achievable sensitivity. Here we explore an alternative search method based on…
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…
The detection and characterization of post-merger gravitational wave signals from binary neutron star mergers remains challenging with current ground-based detectors. We present a convolutional neural network framework designed for…
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…
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…
We show that gravitational-wave signals from compact binary mergers may be better distinguished from instrumental noise transients by using Bayesian models that look for signal coherence across a detector network. This can be achieved even…
Data from gravitational-wave (GW) detectors often contains a high rate of non-Gaussian transient noise, known as glitches. The parameters estimated from GW signals coinciding with detector glitches are occasionally biased away from their…
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,…
Excess noise from scattered light poses a persistent challenge in the analysis of data from gravitational wave detectors such as LIGO. We integrate a physically motivated model for the behavior of these "glitches" into a standard Bayesian…
We present an overview of the SkyMapper optical follow-up program for gravitational-wave event triggers from the LIGO/Virgo observatories, which aims at identifying early GW170817-like kilonovae out to $\sim 200$ Mpc distance. We describe…
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…
Gravitational-wave detection pipelines have helped to identify over one hundred compact binary mergers in the data collected by the Advanced LIGO and Advanced Virgo interferometers, whose sensitivity has provided unprecedented access to the…