4 papers
Assessment of normalizing flows for parameter estimation on time-frequency representations of gravitational-wave data
Daniel Lanchares, Osvaldo G. Freitas, Lysiane Mornas +4
The speed-up of parameter estimation is an active field of research in gravitational-wave data analysis. In this paper we present GP15, a deep-learning method that merges residual…
An autoencoder-based surrogate waveform model for quasi-circular binary-black-hole mergers
Anastasios Theodoropoulos, Nino Villanueva, Osvaldo Gramaxo Freitas +6
The generation of accurate waveforms from binary black hole (BBH) mergers is a major effort in Gravitational-Wave Astronomy. In recent years, machine-learning-based surrogate model…
A Deep Learning Powered Numerical Relativity Surrogate for Binary Black Hole Waveforms
Osvaldo Gramaxo Freitas, Anastasios Theodoropoulos, Nino Villanueva +6
Gravitational-wave approximants are essential for gravitational-wave astronomy, allowing the coverage binary black hole parameter space for inference or match filtering without cos…
Parameter estimation of microlensed gravitational waves with Conditional Variational Autoencoders
Roberto Bada-Nerin, Oleg Bulashenko, Osvaldo Gramaxo Freitas +1
Gravitational lensing of gravitational waves (GWs) provides a unique opportunity to study cosmology and astrophysics at multiple scales. Detecting microlensing signatures, in parti…