Matthew Ho

In this analysis, we apply our neural network methodology to recover a dynamical mass estimate of the famous Coma cluster. We utilize our approximate Bayesian uncertainty methods to predict Coma's mass to be 10^{14.92 ± 0.12} h^-1M_⊙ within 1.55 ± 0.02 h^-1 Mpc of its center. We find that these predictions are statistically consistent with historical estimates of Coma's virial mass, with strong consistency among estimates of the past two decades.

We study methods for reconstructing Bayesian uncertainties on dynamical mass estimates of galaxy clusters using Convolutional Neural Networks (CNNs). We show that Bayesian CNNs produce highly accurate dynamical cluster mass posteriors. These model posteriors are log-normal in cluster mass and recover 68% and 90% confidence intervals to within 1% of their measured value. We note how this rigorous modelling of dynamical mass posteriors is necessary for using cluster abundance measurements to constrain cosmological parameters.

We demonstrate the ability of Convolutional Neural Networks (CNNs) to mitigate systematics in the virial scaling relation and produce dynamical mass estimates of galaxy clusters with remarkably low bias and scatter. We present two models, CNN1D and CNN2D, which leverage this deep learning tool to infer cluster masses from distributions of member galaxy dynamics.

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