相关论文: Improving gravitational wave search sensitivity wi…

Gravitational waves are ripples in the space time fabric when high energy events such as black hole mergers or neutron star collisions take place. The first Gravitational Wave (GW) detection (GW150914) was made by the Laser Interferometer…

Searches for gravitational waves produced by coalescing black hole binaries with total masses $\gtrsim25\,$M$_\odot$ use matched filtering with templates of short duration. Non-Gaussian noise bursts in gravitational wave detector data can…

Machine learning can be a powerful tool to discover new signal types in astronomical data. We here apply it to search for long-duration transient gravitational waves triggered by pulsar glitches, which could yield physical insight into the…

Leveraging long-term user behavioral patterns is a key trajectory for enhancing the accuracy of modern recommender systems. While generative recommender systems have emerged as a transformative paradigm, they face hurdles in effectively…

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…

Significant human and observational resources have been dedicated to electromagnetic followup of gravitational-wave events detected by Advanced LIGO and Virgo. As the sensitivity of LIGO and Virgo improves, the rate of sources detected will…

When a gravitational wave is detected by Advanced LIGO/Virgo, sophisticated parameter estimation (PE) pipelines spring into action. These pipelines leverage approximants to generate large numbers of theoretical gravitational waveform…

Mergers of stellar-mass black holes on highly eccentric orbits are among the targets for ground-based gravitational-wave detectors, including LIGO, VIRGO, and KAGRA. These sources may commonly form through gravitational-wave emission in…

Broadband noise in gravitational wave (GW) detectors, also known as triggers, can often be a deterrant to the efficiency with which astrophysical search pipelines detect sources. It is important to understand their instrumental or…

The Rapid Iterative FiTting (RIFT) parameter inference algorithm provides a framework for efficient, highly-parallelized parameter inference for GW sources. In this paper, we summarize essential algorithm enhancements and operating point…

The LIGO observatories detect gravitational waves through monitoring changes in the detectors' length down to below $10^{-19}$\,$m/\sqrt{Hz}$ variation---a small fraction of the size of the atoms that make up the detector. To achieve this…

The reliability of the first detection is one of the most interesting challenges for the gravitational wave community. To increase the detection confidence, the LIGO and Virgo collaborations have already started coincident observations…

GstLAL is a stream-based matched-filtering search pipeline aiming at the prompt discovery of gravitational waves from compact binary coalescences such as the mergers of black holes and neutron stars. Over the past three observation runs by…

The LIGO-Virgo-KAGRA collaboration uses multiple independent search pipelines to detect gravitational waves, often resulting in multiple triggers (g-events) for a single astrophysical source. These triggers are grouped into superevents,…

A new family of restricted post-Newtonian-accurate waveforms, termed TaylorEt approximants, was recently proposed for searching gravitational wave (GW) signals from inspiraling non-spinning compact binaries having arbitrary mass-ratios. We…

This thesis presents advancements in the detection of gravitational waves from compact binary coalescences, utilising the most sensitive observatories constructed to date. The research focuses on enhancing gravitational-wave signal searches…

Starting from May 2023, the LIGO Scientific, Virgo and KAGRA Collaboration is planning to conduct the fourth observing run with improved detector sensitivities and an expanded detector network including KAGRA. Accordingly, it is vital to…

Deep learning method develops very fast as a tool for data analysis these years. Such a technique is quite promising to treat gravitational wave detection data. There are many works already in the literature which used deep learning…

The advent of deep machine learning platforms such as Tensorflow and Pytorch, developed in expressive high-level languages such as Python, have allowed more expressive representations of deep neural network architectures. We argue that such…

Matched filtering is used to search for gravitational waves emitted by inspiralling compact binaries in data from the ground-based interferometers. One of the key aspects of the detection process is the design of a template bank that covers…