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We present an algorithm for inferring the dynamical mass of galaxy clustersdirectly from their respective phase-space distributions, i.e. the observedline-of-sight velocities and projected distances of galaxies from the clustercentre. Our method employs normalizing flows, a deep neural network capable oflearning arbitrary high-dimensional probability distributions, and inherentlyaccounts, to an adequate extent, for the presence of interloper galaxies whichare not bounded to a given cluster, the primary contaminant of dynamical massmeasurements. We validate and showcase the performance of our neural flowapproach to robustly infer the dynamical mass of clusters from a realistic mockcluster catalogue. A key aspect of our novel algorithm is that it yields theprobability density function of the mass of a particular cluster, therebyproviding a principled way of quantifying uncertainties, in contrast toconventional machine learning approaches. The neural network mass predictions,when applied to a contaminated catalogue with interlopers, have a mean overalllogarithmic residual scatter of 0.028 dex, with a log-normal scatter of 0.126dex, which goes down to 0.089 dex for clusters in the intermediate to high massrange. This is an improvement by nearly a factor of four relative to theclassical cluster mass scaling relation with the velocity dispersion, andoutperforms recently proposed machine learning approaches. We also apply ourneural flow mass estimator to a compilation of galaxy observations of somewell-studied clusters with robust dynamical mass estimates, furthersubstantiating the efficacy of our algorithm.

Dynamical mass inference of galaxy clusters with neural flows