Galaxy shape measurement synergies between LSST and Euclid

TL;DR

Combining LSST-like ground-based and Euclid-like space-based imaging data enhances galaxy shape measurement precision and increases effective galaxy density for cosmic shear analysis, especially for faint and small galaxies.

Contribution

This study demonstrates the benefits of joint-pixel analysis over catalogue-level combination for galaxy shape measurements in combined LSST and Euclid surveys.

Findings

Joint analysis increases effective galaxy density by ~50% at i~24.5.

Joint-pixel analysis improves shape measurement precision by ~20%.

Effective galaxy density for lensing improves by ~5%.

Abstract

We demonstrate that a joint analysis of LSST-like ground-based imaging with Euclid-like space-based imaging leads to increased precision and accuracy in galaxy shape measurements. At galaxy magnitudes of $i \sim 24.5$, a combined survey analysis increases the effective galaxy number density for cosmic shear studies by $\sim 50$ percent in comparison to an analysis of each survey alone. Using a realistic distribution of galaxy sizes, ellipticities and magnitudes down to $i = 25.2$, we simulate LSST-like and Euclid-like images of over one million isolated galaxies. We compare the precision and accuracy of the recovered galaxy ellipticities for four different analyses: LSST-only, Euclid-only, a simultaneous joint-pixel analysis of the two surveys, and a simple catalogue-level survey combination. In the faint and small-galaxy regime, where neither survey excels alone, we find a $\sim 20$ percent increase in the precision of galaxy shape measurement when we adopt a joint-pixel analysis, compared to a catalogue-level combination. As the statistical power of cosmic shear is dominated by intrinsic ellipticity noise, however, this improvement in shape measurement noise only leads to a $\sim 5$ percent improvement in the effective number density of galaxies for lensing studies. We view this as the minimum improvement that should be expected from a joint-pixel analysis over a less accurate catalogue-level combination, as the former will also improve the capability of LSST to de-blend close objects.