Astraea is a package to train Random Forest (RF) models on datasets. It provides tools to train RF classifiers and regressors as well as perform simple cross-validation tests and performance plots on the test set.
It was first developed to calculate rotation period of stars from various stellar properties provided and is intended to predict long rotation periods (e.g. that of M-dwarfs) from short TESS lightcurves (27-day lightcurves).
We provide access to trained models on stars from the catalog by McQuillian et all. (2014). User can predict whether the rotation period can be recovered and measure recoverable rotation periods for the stars in the Kepler field by using their temperatures, colors, kinematics, etc.
- Predict rotation period for Kepler stars using existing model
- Train a regressor model and test its performance
- Train a classifier model and plot the Receiver operating characteristic (ROC) curve
License & attribution¶
Copyright 2020, Yuxi Lu.
The source code is made available under the terms of the MIT license.
If you make use of this code, please cite this package and its dependencies. You can find more information about how and what to cite in the citation documentation (not yet complete).