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 

```{eval-rst}
.. toctree::
   :caption: Getting started
   :maxdepth: 1

   First pipeline <first_pipeline>
   Contributing <contributing>

.. toctree::
   :caption: Code Structure
   :maxdepth: 1

   Tasks <tasks>
   Projects <projects>
   Libraries <libraries>

.. toctree::
   :caption: Advanced
   :maxdepth: 1

   Remote Training <remote>
   Tuning <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"](https://arxiv.org/abs/2403.18661). Please also cite this paper if you use `Aframe` software in your work.