https://github.com/gully/helloworldnet

Finding new worlds from Kepler/TESS data with PyTorch-- A fork of ExoNet from Ansdell et al. 2018
https://github.com/gully/helloworldnet

Last synced: 11 months ago
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Finding new worlds from Kepler/TESS data with PyTorch-- A fork of ExoNet from Ansdell et al. 2018

• Host: GitHub
• URL: https://github.com/gully/helloworldnet
• Owner: gully
• Created: 2019-08-08T21:09:14.000Z (almost 7 years ago)
• Default Branch: master
• Last Pushed: 2019-12-19T02:46:58.000Z (over 6 years ago)
• Last Synced: 2025-04-15T18:51:06.478Z (about 1 year ago)
• Language: Jupyter Notebook
• Homepage: https://gitlab.com/frontierdevelopmentlab/exoplanets/exonet-pytorch
• Size: 81.3 MB
• Stars: 13
• Watchers: 5
• Forks: 1
• Open Issues: 2
• Metadata Files:
  • Readme: README.md

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README

          
# HelloWorldNet

![Kepler Data](https://github.com/google-research/exoplanet-ml/raw/master/exoplanet-ml/astronet/docs/transit.gif)

*fig credit: Chris Shallue*

HelloWorldnet is a modified version of [Exonet](https://gitlab.com/frontierdevelopmentlab/exoplanets/exonet-pytorch), which is in turn a modified version of [Astronet](https://github.com/tensorflow/models/tree/master/research/astronet)

This work is a direct result of the [2019 PyTorch Summer Hackathon](https://info.devpost.com/pytorchmpkrules), hosted at Facebook HQ, with team members:

- [Gully](https://github.com/gully)

- [Grant](https://github.com/GrantRVD) ([twitter](https://twitter.com/usethespacebar))

- [Humayun](https://github.com/humayun)

Our goal is to apply PyTorch to improve the speed and reliability of detecting exoplanets in [lightcurve](https://imagine.gsfc.nasa.gov/features/yba/M31_velocity/lightcurve/lightcurve_more.html) 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

### Performance Benchmark

| 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**|

![Training Curves for HelloWorldNet](./graphs/Training.png)

### Finding planets is a needle in a haystack problem



### Neural networks can distinguish rare exoplanets from spurious astrophysical signals

We used data from the Gaia Mission Data Release 2 to improve our knowledge of the stars, making the model more accurate and precise.

![PyTorch Helps](graphs/gaiaDR2_neural_network.png)

### Training Performance

![how we did](graphs/pytorch_hackathon_performance.png)