Random forests reproduce simulated halo gas density profiles to roughly 80-90% accuracy, and Sobol analysis of those forests ranks halo mass and central gas mass as the dominant predictors across EAGLE, IllustrisTNG, and Simba.
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Interpretable machine learning of halo gas density profiles: a sensitivity analysis of cosmological hydrodynamical simulations
Random forests reproduce simulated halo gas density profiles to roughly 80-90% accuracy, and Sobol analysis of those forests ranks halo mass and central gas mass as the dominant predictors across EAGLE, IllustrisTNG, and Simba.