Kinematic Lensing Inference II: Cluster Lensing with $\mathcal{O}$(1) Galaxies

TL;DR

This paper demonstrates the first detection of a cluster lensing signal using Kinematic Lensing, a novel method combining photometry, spectroscopy, and galaxy dynamics, achieving significant improvements in shear measurement precision.

Contribution

It provides the first observational application of Kinematic Lensing to galaxy clusters, validating the method with real data and comparing results to traditional weak lensing.

Findings

Shear estimates from Kinematic Lensing agree broadly with traditional methods.

Achieved a ten-fold reduction in shear measurement uncertainty.

Identified target selection and observing strategy as key for future improvements.

Abstract

We present the first detection of a cluster lensing signal with `Kinematic Lensing' (KL), a novel weak lensing method that combines photometry, spectroscopy, and the Tully-Fisher relation to enable shear measurements with individual source galaxies. This is the second paper in a two-part series aimed at measuring a KL signal from data. The first paper, arXiv:2209.11811, describes the inference pipeline, which jointly forward models galaxy imaging and spectroscopy, and demonstrates unbiased shear inference with simulated data. This paper presents measurements of the lensing signal from the galaxy cluster Abell 2261. We obtain spectroscopic observations of background disk galaxies in the cluster field selected from the CLASH Subaru catalog. The final sample consists of three source galaxies while the remaining are rejected due to insufficient signal-to-noise, spectroscopic failures, and inadequately sampled rotation curves. We apply the KL inference pipeline to the three sources and find the shear estimates to be in broad agreement with traditional weak lensing measurements. The typical shear measurement uncertainty for our sources is $\sigma(g_+)\approx 0.026$, which represents approximately a ten-fold improvement over the weak lensing shape noise. We identify target selection and observing strategy as the key avenues of improvement for future KL programs.