Reconstructing Projected Matter Density from Cosmic Microwave Background
Matias Zaldarriaga, Uros Seljak
TL;DR
This work develops a direct 2‑D reconstruction of the projected matter density $\kappa$ from gravitational lensing of the cosmic microwave background by using quadratic combinations of CMB temperature derivatives. The authors formulate a formalism with derivative pairs ${\cal S}$, ${\cal Q}$, ${\cal U}$ that couple to $\kappa$ and the shear, and derive estimators for the convergence power spectrum $C_l^{\kappa\kappa}$ in both large‑ and small‑scale limits, including noise and beam effects. Through simulations, they demonstrate cluster mass‑profile recovery when small‑scale power (e.g., OV effect) is present, and show that even without a direct map of $\kappa$, the power spectrum can be recovered or cross‑correlated with external tracers to yield detections. They also analyze practical requirements for MAP/Planck observations and highlight cross‑correlation opportunities with SZ, X‑ray background, and galaxy surveys to enhance the cosmological leverage of the method.
Abstract
Gravitational lensing distorts the cosmic microwave background (CMB) anisotropies and imprints a characteristic pattern onto it. The distortions depend on the projected matter density between today and redshift $z \sim 1100$. In this paper we develop a method for a direct reconstruction of the projected matter density from the CMB anisotropies. This reconstruction is obtained by averaging over quadratic combinations of the derivatives of CMB field. We test the method using simulations and show that it can successfully recover projected density profile of a cluster of galaxies if there are measurable anisotropies on scales smaller than the characteristic cluster size. In the absence of sufficient small scale power the reconstructed maps have low signal to noise on individual structures, but can give a positive detection of the power spectrum or when cross correlated with other maps of large scale structure. We develop an analytic method to reconstruct the power spectrum including the effects of noise and beam smoothing. Tests with Monte Carlo simulations show that we can recover the input power spectrum both on large and small scales, provided that we use maps with sufficiently low noise and high angular resolution.
