BEGIN:VCALENDAR VERSION:2.0 PRODID:-//linuxsoftware.nz//NONSGML Joyous v1.4//EN BEGIN:VEVENT SUMMARY:Gravitational data analysis with machine learning and deep learnin g DTSTART:20250918T143000Z DTEND:20250918T160000Z DTSTAMP:20260416T210225Z UID:0f21baa0-99d2-4d7a-8373-1eb0f7da7de6 SEQUENCE:2 CREATED:20250915T133021Z DESCRIPTION:Gravitational-wave astronomy enables precision studies of comp act binary systems and provides powerful tests of General Relativity. Blac k-hole spectroscopy—the detection of gravitational-wave emission spectra from black-hole ringdowns—offers a particularly clean framework for tes ting gravity theories against well-defined predictions. However\, accurate waveform modeling and efficient parameter estimation are essential to ext ract both astrophysical and fundamental physics insights from the data. In this talk\, I will present my recent works in gravitational waveform mode ling and inference\, with a focus on black hole ringdowns\, and highlighti ng the role of machine learning and deep learning techniques. In particula r\, I will discuss the promises and challenges of using Gaussian Process R egression for waveform modeling and hierarchical inference\, and simulatio n-based inference for parameter estimation. LAST-MODIFIED:20250915T133040Z LOCATION:DF Seminar Room (2-8.3)\, 2nd floor of Physics Building URL:http://df.vps.tecnico.ulisboa.pt/pt/eventos/gravitational-data-analysi s-with-machine-learning-and-deep-learning/ X-ALT-DESC;FMTTYPE=text/html:

Gravitational-wave astronomy enables precision studies of compact binary systems and provides powerful tests of General Relativity. Black-hole spectroscopy—the detec tion of gravitational-wave emission spectra from black-hole ringdowns—of fers a particularly clean framework for testing gravity theories against w ell-defined predictions.

However\, accurate waveform modeling an d efficient parameter estimation are essential to extract both astrophysic al and fundamental physics insights from the data. In this talk\, I will p resent my recent works in gravitational waveform modeling and inference\, with a focus on black hole ringdowns\, and highlighting the role of machin e learning and deep learning techniques.

In particular\, I will discuss the promises and challenges of using Gaussian Process Regression f or waveform modeling and hierarchical inference\, and simulation-based inf erence for parameter estimation.

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