kSZ Pairwise Velocity Reconstruction with Machine Learning

Abstract

We demonstrate that pairwise peculiar velocity correlations for galaxy clusters can be directly reconstructed from the kinematic Sunyaev-Zel'dovich (kSZ) signature imprinted in the CMB using a machine learning model with a gradient boosting algorithm trained on high-fidelity kSZ simulations. The machine learning model is trained using six to eight cluster features that are directly related to observables from CMB and large-scale structure surveys. We validate the capabilities of the approach in light of the presence of primary CMB, detector noise, and potential uncertainties in the cluster mass estimate and cluster center location. The pairwise velocity statistics extracted using the techniques developed here have the potential to elicit valuable cosmological constraints on dark energy, modified gravity models, and massive neutrinos with kSZ measurements from upcoming CMB surveys, including the Simons Observatory, CMB-S4 and CCAT, and the DESI and SDSS galaxy surveys.