A ML-based galaxy group finder and dark matter halo mass estimator for MOONRISE-type data large datasets.
Project description
Dark from Light 🌌
"Unveiling the invisible universe, one galaxy at a time" A Python package that classifies galaxies into groups and estimates dark matter halo masses using Random Forest regressors trained on large volume cosmological simulations: EAGLE and IllustrisTNG.
Features
- Galaxy Group Classification: Automatically allocate galaxies into groups
- Dark Matter Mass Estimation: Predict halo masses from observable properties (photometric/spectroscopic stellar mass estimates, sky coordinates and redhsift)
- Pre-trained Models: Ready-to-use Random Forest regressors trained on simulation data (mapping between electromagnetic observables and halo properties)
- Multiple Halo Mass - Stellar Mass Relations: Choice among models trained on EAGLE, IllustrisTNG and hybrid approach (mean prediction between the two simulations)
Quick Start
from dark_from_light import allocate_galaxy_groups, HybridRegressionModel
# Classify galaxies into groups
allocate_galaxy_groups(galaxy_data)
# Estimate dark matter halo masses
model = HybridRegressionModel()
halo_mass = model.predict_group(group_total_stellar_mass)
Turn stellar observations into cosmic insights.
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