Convolutional Neural Network-reconstructed velocity for kinetic SZ
  detection
  By stacking the latest 217~GHz Planck PR4 map at the positions of 30,431
galaxy clusters from the Wen-Han-Liu (WHL) catalog, we report the detection of
the kinetic Sunyaev-Zel'dovich (kSZ) effect in galaxy clusters with a 4.9 sigma
significance level and beyond 3xR500. The line of sight velocities of galaxy
clusters were estimated with a machine-learning approach, in which the relation
between the galaxy distribution around a cluster and its line-of-sight velocity
was trained through a convolutional neural network using the simulated galaxies
and galaxy clusters in the Magneticum cosmological hydrodynamic simulations.
The trained model was applied to the large-scale distribution of the Sloan
Digital Sky Survey galaxies to derive the line-of-sight velocities of the WHL
galaxy clusters. Assuming a standard beta-model for the intracluster medium, we
found that the gas fraction within R500 is fgas,500 = 0.09+-0.02 for the
clusters with the mass of M500~1.0x10^14 Msun.
