Conveners
Session III
- Carlos Delgado (CIEMAT)
This work explores feature selection for glitch classification in gravitational wave detectors, where transient noise events are grouped by their time-frequency morphology. Gravity Spy is a citizen-science project that provides a standard dataset built from four Q-transform views of glitches. Follow-up studies introduced attention-based multi-view models, transfer learning, and examined the...
The speed-up of parameter estimation is an active field of research in gravitational-wave data analysis. In this paper we present GP12, a deep-learning method that merges residual networks and normalising flows into a general-purpose, image-based estimator of binary black hole (BBH) parameters. Building on our early work we map BBH spectrograms from the Advanced LIGO and Advanced Virgo...
Source inference for deterministic gravitational waves is a computationally demanding task in LISA. In a novel approach, we investigate the capability of an active-learning Gaussian Processes-based framework to learn the posterior for source parameters, using a very small number of likelihood evaluations, and without the need for pretraining. We benchmark our method against a cutting-edge...
We develop a framework for accelerated parameter estimation for lensed gravitational waves in the wave-optics regime. To model these effects, we evaluate the amplification factor using GLoW, an accurate code that computes the frequency-dependent amplification factor for generic lens models and arbitrary impact parameters depending on the lens configuration. To extract physical parameters from...
