https://github.com/racarvajal/ml_prediction_pipeline_run

Example code to run prediction pipeline from Carvajal et al. 2023
https://github.com/racarvajal/ml_prediction_pipeline_run

agn astrophysics learning machine machine-learning ml prediction radio-galaxies redshift

Last synced: about 1 month ago
JSON representation

Example code to run prediction pipeline from Carvajal et al. 2023

• Host: GitHub
• URL: https://github.com/racarvajal/ml_prediction_pipeline_run
• Owner: racarvajal
• Created: 2021-10-08T14:07:17.000Z (about 3 years ago)
• Default Branch: main
• Last Pushed: 2023-12-01T16:42:08.000Z (about 1 year ago)
• Last Synced: 2023-12-01T22:23:41.221Z (about 1 year ago)
• Topics: agn, astrophysics, learning, machine, machine-learning, ml, prediction, radio-galaxies, redshift
• Language: Jupyter Notebook
• Homepage:
• Size: 36 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# Prediction of radio-detectable AGN with Machine Learning pipeline

Jupyter notebook with example script to load and run prediction pipeline from Carvajal et al., 2023. (https://arxiv.org/abs/2309.11652)

Data and model files can be obtained from [Zenodo](https://zenodo.org/records/10220009)

* `running_pred_pipeline.ipynb` contains a simple code to download the files and run the prediction pipeline on them.

* `global_variables.py` contains names and paths of files for running the code.

* `global_functions.py` contains a few functions to run each step of the prediction pipeline.