Matched-filtering techniques are the standard approach for detecting compact binary coalescences (CBCs) and have been employed in all confirmed gravitational-wave (GW) detections to date. However, these methods rely on large banks of accurately modeled waveform templates, making them unsuitable for poorly modeled or unmodeled sources such as core-collapse supernovae (CCSN) and other unknown...
By leveraging a high performance noise reduction algorithm and the bespoke deep-learning architectures developed by us, we are constructing a pipeline for searching anomalous gravitational wave bursts. The pipeline consists of three parts: BEACON, DeepGRAV and GenGRAV. I will introduce the basic concepts of this work in this talk.
In this talk, I will introduce the development of the unmodeled search pipeline BEACON. The pipeline has a block-wise structure that consists of four stages: denoising, anomaly clustering, significance evaluation, and coincidence analysis. We tested the framework on GWTC-1 BBH events and off-catalog BBH signals from the O2 run. With its efficient computation, the pipeline demonstrates...
I'll present a method to optimize the analysis of long-duration Gravitational Waves (GWs) from compact binary coalescences (CBCs). A typical example is GWs from compact objects with masses below that of the Sun. The LIGO–Virgo–KAGRA (LVK) collaboration, operating the world’s most sensitive GW observatories, searches for CBC signals in the 0.2–1.0 solar mass range, providing leading constraints...
