Galaxy Environment Length Scales

An exploration of galaxy environments in the context of the galaxy-halo connection using explainable AI and graph neural networks

Data

We make use of the SUBFIND snapshot 99 (redshift 0) subhalo catalogs derived from the Illustris TNG300-1 hydrodynamic and dark matter only (DMO) simulations. All data can be accessed through the IllustrisTNG website.

Requirements

The most important requirements are pytorch and pytorch-geometric; check out the latter's documentation for more information about installing it.

Training and interpreting the explainable boosting machine (EBM) models requires the interpret and shap packages.

Code

All of the GNN code is contained in ./src, and the full results can be found in ./notebook/results.ipynb. For example, if you want to train a GNN with multi-aggregation no self-loops to map dark matter only subhalo properties to galaxy stellar masses, then run:

python src/painting_galaxies.py --aggr multi --loops 0 --mode dmo

Training the EBM is fairly straightforward, and you can see examples in the notebook, e.g.:

ebm_hyperparams = {
    "max_bins": 50000, 
    "validation_size": 0.3,
    "interactions": 32,
}

ebm = ExplainableBoostingRegressor(**ebm_hyperparams)        
ebm.fit(X_train, y_train)

y_pred = ebm.predict(X_valid)

Citation

Our paper has now been submitted to ApJ! If you use this code, please cite:

@ARTICLE{2024arXiv240207995W,
       author = {{Wu}, John F. and {Kragh Jespersen}, Christian and {Wechsler}, Risa H.},
        title = "{How the Galaxy-Halo Connection Depends on Large-Scale Environment}",
      journal = {arXiv e-prints},
     keywords = {Astrophysics - Astrophysics of Galaxies, Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics},
         year = 2024,
        month = feb,
          eid = {arXiv:2402.07995},
        pages = {arXiv:2402.07995},
archivePrefix = {arXiv},
       eprint = {2402.07995},
 primaryClass = {astro-ph.GA},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2024arXiv240207995W},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

Acknowledgments

This project was made possible by the KITP Program, Building a Physical Understanding of Galaxy Evolution with Data-driven Astronomy (see also the website).