Aframe

Detecting compact binary mergers from gravitational wave strain data using neural networks, with an emphasis on

• Efficiency - making effective use of accelerated hardware like GPUs in order to minimize time-to-solution

• Scale - validating hypotheses on large volumes of data to obtain high-confidence estimates of model performance

• Flexibility - modularizing functionality to expose various levels of abstraction and make implementing new ideas simple

• Physics first - taking advantage of the rich priors available in GW physics to build robust models and evaluate them accoring to meaningful metrics

• Multi-messenger astronomy - making algorithmic decisions and optimizations that allow for extremely low-latency alerts

Getting started

• First pipeline
• Contributing

Code Structure

• Tasks
• Projects
• Libraries

Advanced

• Remote Training
• Tuning

For algorithm details and performance estimates on the LVK O3 observing run, please see “A machine-learning pipeline for real-time detection of gravitational waves from compact binary coalescences”. Please also cite this paper if you use Aframe software in your work.