Speaker
Description
The 2030s are anticipated to be a golden era in ground-based gravitational wave astronomy, with the advent of next-generation observatories such as the Einstein Telescope and Cosmic Explorer set to revolutionize our understanding of the universe. However, this unprecedented sensitivity and observational depth will come with a significant increase in the computational demands of gravitational-wave data analysis. Traditional pipelines based on nested sampling (NS), renowned for its reliability and robustness in parameter estimation and model comparison, will soon become infeasible for the volume and complexity of this future data. The introduction of Graphics Processing Units (GPUs) has transformed scientific computing. In particular, implementing nested sampling within a GPU-accelerated pipeline promises to overcome the scaling challenges posed by next-generation data. We demonstrate the accuracy and efficiency of the newly developed blackjax implementation of NS (Yallup et al.) in analysing simulated gravitational-wave data.
| AI keywords | simulation-based inference |
|---|
