Repository for CNN models for galaxy cluster mass estimation as presented in:
- A Robust and Efficient Deep Learning Method for Dynamical Mass Measurements of Galaxy Clusters.
- Approximate Bayesian Uncertainties on Deep Learning Dynamical Mass Estimates of Galaxy Clusters
- The dynamical mass of the Coma cluster from deep learning
- Benchmarks and Explanations for Deep Learning Estimates of X-ray Galaxy Cluster Masses
This repository is meant to serve as an example of the code used to perform this deep learning analysis, not as a fully-developed utility for public use.
The mock cluster catalog generation script is make_mocks.py. It uses halo data from the MultiDark Planck 2 simulation Rockstar catalog and a galaxy catalog generated using UniverseMachine. The generated catalogs are stored as Catalog objects, detailed in catalog.py. For a full previously-generated mock catalog, reach out to the corresponding author at matthew.annam.ho@gmail.com.
Data processing is handled by the HaloCNNDataManager classes in data. Models are represented as the BaseHaloCNNRegressor class in model.