Speaker
Waleed Esmail
(University of Münster)
Description
Future gravitational-wave observatories will operate in a regime of unprecedented sensitivity, long-duration signals, and complex environmental noise. In this talk, I will discuss recent developments in applying machine learning to gravitational-wave data analysis, with a focus on deep-learning models for signal detection, the separation and reconstruction of overlapping long-duration signals, and data-driven waveform generation. I will also highlight the role of machine learning in characterizing and mitigating seismic and environmental noise, particularly in the context of next-generation detectors such as the Einstein Telescope.
Author
Waleed Esmail
(University of Münster)
