Kinematic lensing inference - I. Characterizing shape noise with simulated analyses

R. S. Pranjal, Elisabeth Krause, Hung Jin Huang, Eric Huff, Jiachuan Xu, Tim Eifler, Spencer Everett

Research output: Contribution to journalArticlepeer-review

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, whereas the shear effect of interest is of the order of per cent. 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 depending on emission-line signal-to-noise ratio. This order of magnitude improvement over traditional WL makes a KL observational programme 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.

Original languageEnglish (US)
Pages (from-to)3324-3334
Number of pages11
JournalMonthly Notices of the Royal Astronomical Society
Volume524
Issue number3
DOIs
StatePublished - Sep 1 2023

Keywords

  • gravitational lensing: weak
  • large-scale structure of Universe
  • methods: statistical

ASJC Scopus subject areas

  • Astronomy and Astrophysics
  • Space and Planetary Science

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