fig credit: Chris Shallue
HelloWorldnet is a modified version of Exonet, which is in turn a modified version of Astronet
This work is a direct result of the 2019 PyTorch Summer Hackathon, hosted at Facebook HQ, with team members:
Our goal is to apply PyTorch to improve the speed and reliability of detecting exoplanets in lightcurve data. Specifically, we're attempting to
- extend Exonet and Astronet for better precision and recall
- creating dataloaders for various data sources, such as Kepler, TESS, and K2
- exploring model architectures to improve transfer learning between exoplanet monitoring and detection tasks
| Model | Avg. Precision |
|---|---|
| Astronet (TensorFlow) | 0.955 |
| Exonet (PyTorch) Replication | 0.969 |
| Exonet (PyTorch) Reported (Ansdell et al. (2018)) | 0.980 |
| HelloWorldNet (PyTorch Hackathon) | 0.977 |
We used data from the Gaia Mission Data Release 2 to improve our knowledge of the stars, making the model more accurate and precise.
