New Ways to Use Dynamical Measurements of Galaxy Clusters
Hy Trac (CMU)
March 2 @ 4:00 PM - 5:00 PM
Galaxy clusters contain large amounts of cold dark matter, hot ionized gas, and tens to hundreds of visible galaxies. The abundance of clusters as a function of mass and redshift can be used to probe the growth of structure and constrain cosmological parameters. Dynamical measurements probe the entire mass distribution, but standard analyses yield unwanted high mass errors. First we show that modern machine learning algorithms can improve mass measurements by more than a factor of two compared to using standard scaling relations. Support Distribution Machines are used to train and test on the entire distribution of galaxy velocities to maximally use available information. Second we show that cluster abundance can be quantified with the distribution of direct observables rather than inferred mass to avoid uncertainties in the mass-observable relation. A novel statistic called the velocity distribution function (VDF) is constructed by stacking the probability distribution of galaxy velocities for a select number of clusters in a given volume. The VDF can be measured directly and precisely, and produces unbiased constraints on cosmological parameters. Finally we discuss how our approaches can be generalized to multi-wavelength observations of gravitational lensing, SZ effect, and X-ray emissions.