Maximum likelihood kinetic Sunyaev Zel'dovich velocity reconstruction

TL;DR

This paper introduces a maximum likelihood estimator for reconstructing the radial velocity field from the kSZ effect, outperforming previous methods in low-noise, high-resolution CMB experiments, enabling better cosmological tests.

Contribution

It presents a novel maximum likelihood approach for kSZ velocity reconstruction, improving fidelity over quadratic estimators in upcoming high-resolution CMB data.

Findings

Higher fidelity reconstruction in low-noise regimes

Implementation in harmonic and map space

Potential for enhanced cosmological tests

Abstract

The kinetic Sunyaev Zel'dovich (kSZ) effect, cosmic microwave background (CMB) temperature anisotropies induced by the scattering of CMB photons from free electrons, will be measured by near-term CMB experiments at high significance. By combining CMB temperature anisotropies with a tracer of structure, such as a galaxy redshift survey, previous literature introduced a number of techniques to reconstruct the radial velocity field. This reconstructed radial velocity field encapsulates the majority of the cosmological information contained in the kSZ temperature anisotropies, and can provide powerful new tests of the standard cosmological model and theories beyond it. In this paper, we introduce a new estimator for the radial velocity field based on a coarse-grained maximum likelihood fit for the kSZ component of the temperature anisotropies, given a tracer of the optical depth. We demonstrate that this maximum likelihood estimator yields a higher fidelity reconstruction than existing quadratic estimators in the low-noise and high-resolution regime targeted by upcoming CMB experiments. We describe implementations of the maximum likelihood estimator in both harmonic space and map space, using either direct measurements of the optical depth field or a galaxy survey as a tracer. We comment briefly on the impact of biases introduced by imperfect reconstruction of the optical depth field.