Speaker
Description
Gravitational-wave detectors such as LIGO, Virgo, and KAGRA, detect faint signals from distant astrophysical events. However, their high sensitivity also exposes them to noise artifacts — including unmodelled transients known as "glitches" — that can mimic or mask genuine signals. Last year we introduced DeepExtractor, a deep learning framework that reconstructs arbitrary signals or glitches with power above the detector noise floor, regardless of its source or morphology, by learning to model and subtract the underlying detector noise rather than the signal itself.
In this talk we present extensions to the framework. We extend the method to the case where a signal and a glitch overlap in time and frequency, by jointly modelling both components alongside the background noise so that each can be reconstructed via subtraction, and show that this separation step improves downstream parameter estimation on the recovered signal by reducing the glitch bias. Finally, we discuss DeepExtractor's suitability for online, low-latency, template-free searches, where its morphology-agnostic reconstruction offers a potential alternative to matched-filtering and unmodelled search pipelines.