4 papers
A fast, differentiable neural-network surrogate for precessing binary black-hole waveforms
Beka Modrekiladze
Gravitational-wave parameter estimation requires millions of waveform evaluations per event, a cost that constrains real-time inference and population studies. We present a fast, f…
On the Motion of Compact Objects in Relativistic Viscous Fluids
Beka Modrekiladze, Ira Z. Rothstein, Jordan Wilson-Gerow
We present a world-line effective field theory of compact objects moving relativistically through a viscous fluid. The theory is valid when velocity gradients are small compared to…
Dual Space Training for GANs: A Pathway to Efficient and Creative Generative Models
Beka Modrekiladze
Generative Adversarial Networks (GANs) have demonstrated remarkable advancements in generative modeling; however, their training is often resource-intensive, requiring extensive co…
Transfer Learning Adapts to Changing PSD in Gravitational Wave Data
Beka Modrekiladze
The detection of gravitational waves has opened unparalleled opportunities for observing the universe, particularly through the study of black hole inspirals. These events serve as…