photo-z CO binary classifier

This strategy of binary classification to identify catastrophic outliers is presented in “Machine Learning Classification to Identify Catastrophic Outlier Photometric Redshift Estimates.” J. Singal, G. Silverman, E. Jones, T. Do, B. Boscoe, and Y. Wan, 2022, Astrophysical Journal, 928, 6

This package contains Jupyter notebooks and supporting files which do the following:

- Perform a neural network regression to estimate photo-zs
(photoz_regression_mlp.ipynb)

- Take a data set with estimated photo-zs, set aside 30% of the galaxies as a base evaluation set, and output training sets for a binary classifier with varying portions of catastrophic outliers using the remaining 70% of the galaxies
(process_data_for_binary_classifier.ipynb)

- Perform a neural network binary classification to determine catastrophic outliers given a data set with photometry and estimated photo-zs
(catastrophic_outlier_binary_classification.ipynb)

Supporting files:
galaxy_utils.py
models.py