Cosmic shear with one component and its application to future radio surveys

TL;DR

This paper introduces one-component Kinematic Lensing (KL), a streamlined weak-lensing technique that uses imaging plus limited kinematic information to break the shape–shear degeneracy and reduce per-galaxy shape noise. It develops a formalism for the one-component shear, deriving how the observable γ' relates to the full shear and how the corresponding power spectra and covariances scale relative to traditional two-component KL, while neglecting non-Gaussian terms. Using SKA2-like forecasts based on the T-RECS radio catalog, the study compares traditional weak lensing with one-component KL for both HI-selected radio samples and deeper spectroscopic-like samples, finding that KL is not yet competitive for HI surveys due to the shallow redshift distribution but could surpass WL with higher-redshift tracers such as optical emission-line kinematics. The results underscore the importance of redshift reach over sheer galaxy number density for one-component KL, and point to optical avenues and survey-efficiency gains as promising directions for future cosmic shear analyses.

Abstract

We present a new approach to measuring cosmic shear: the one-component Kinematic Lensing (KL) method. This technique provides a simplified implementation of KL that reduces shape noise in weak lensing (WL) by combining kinematic information with imaging data, while requiring less observational effort than the full two-component KL. We perform simulated likelihood analyses to assess the performance of the one-component KL and demonstrate its applicability to future radio surveys. Our forecasts indicate that, for radio surveys, the one-component KL is not yet competitive with traditional WL due to the shallow redshift distribution of HI-selected galaxies. However, when applying this method to deeper spectroscopic surveys with stronger emission lines, the one-component KL approach could surpass WL in constraining power, offering a promising and efficient pathway for future shear analyses.

Figures (4)

• Figure 1: An illustration of the KL one component concept. For each disk galaxy, we infer the rotation velocity from photometry and the Tully-Fisher relation. From a spectrum with low spatial resolution, we measure its observed velocity profile and estimate the line-of-sight disk velocity. The ratio between the rotation velocity and the measured line-of-sight velocity is the disk inclination. We then use this estimated inclination angle to infer the intrinsic shape.
• Figure 2: Left: The redshift distribution of the WL sample (green) from the T-RECS continuum catalog. The other three histograms correspond to the distributions of the T-RECS SFGs (black), the galaxies with $\rm SNR_{cont} \geq 10$ (blue), and the galaxies with $a_{\rm maj} \geq 1.5 \,b_{\rm cont}$ (orange). Right: The redshift distribution of the KL sample from the T-RECS HI catalog. The color codes are the same as the left panel while the green histogram is the KL sample with threshold $\rm SNR=10$ and the red histogram has threshold $\rm SNR=5$.
• Figure 3: The marginalized posterior of $S_8$ for WL (red), KL with $\rm SNR\geq10$ (green), and KL with $\rm SNR\geq 5$ (blue).
• Figure 4: Left: Constraint on $S_8$ when fixing the source redshift distribution to the KL ($\rm SNR\geq5$) galaxy redshift distribution (the redshift histogram in the right panel of Fig. \ref{['fig:dndz']}) for both WL (red) and one-component KL (blue). Right: Constraint on $S_8$ when fixing the source number density to $0.856\,\rm arcmin^{-2}$ for both WL (red) and one-component KL (blue). Both scenarios are done by only sampling $\Omega_{\rm m}$ and $\sigma_8$ to speed up the convergence.