Kinematic Lensing Inference I: Characterizing Shape Noise with Simulated Analyses

TL;DR

Kinematic lensing (KL) is a novel technique that combines imaging and spectroscopy to significantly reduce shape noise in weak lensing measurements, enabling more precise gravitational shear inference.

Contribution

This paper introduces a KL inference pipeline that jointly models imaging and spectroscopy, demonstrating a tenfold reduction in shape noise compared to traditional weak lensing methods.

Findings

KL can recover input shear robustly in simulations.

Shape noise in KL is estimated to be 0.022-0.041, much lower than traditional WL.

Prioritizing low inclination galaxy spectra improves measurement accuracy.

Abstract

The unknown intrinsic shape of source galaxies is one of the largest uncertainties of weak gravitational lensing (WL). It results in the so-called shape noise at the level of $\sigma_\epsilon^{\mathrm{WL}} \approx 0.26$, whereas the shear effect of interest is of order percent. Kinematic lensing (KL) is a new technique that combines photometric shape measurements with resolved spectroscopic observations to infer the intrinsic galaxy shape and directly estimate the gravitational shear. This paper presents a KL inference pipeline that jointly forward-models galaxy imaging and slit spectroscopy to extract the shear signal. We build a set of realistic mock observations and show that the KL inference pipeline can robustly recover the input shear. To quantify the shear measurement uncertainty for KL, we average the shape noise over a population of randomly oriented disc galaxies and estimate it to be $\sigma_\epsilon^{\mathrm{KL}}\approx 0.022-0.041$ depending on emission line signal-to-noise. This order of magnitude improvement over traditional WL makes a KL observational program feasible with existing spectroscopic instruments. To this end, we characterize the dependence of KL shape noise on observational factors and discuss implications for the survey strategy of future KL observations. In particular, we find that prioritizing quality spectra of low inclination galaxies is more advantageous than maximizing the overall number density.