Gravitational waveform modeling and parameter estimation with machine learning and deep learning
Gravitational-wave astronomy enables precision studies of compact binary systems and provides powerful tests of General Relativity. Black-hole spectroscopy— the detection of gravitational-wave emission spectra from black-hole ringdowns — offers a particularly clean framework for testing gravity theories against well-defined predictions. However, accurate waveform modeling and efficient parameter estimation are essential to extract both astrophysical and fundamental physics insights from the data. In this seminar, with a focus on black hole ringdowns, I will present recent advances in gravitational waveform modeling and inference, highlighting the role of machine learning and deep learning techniques. In particular, I will discuss the promises and challenges of using Gaussian Process Regression for waveform modeling and simulation-based inference for parameter estimation.
Prof. Umberto D'Alesio - umberto.dalesio@ca.infn.it
Dr. Nanako Kato - nanako.kato@dsf.unica.it