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Galaxy shape measurement synergies between LSST and Euclid

2019/01/24 by Robert L. Schuhmann, Catherine Heymans, Schuhmann, Robert L. +3 · 1 citation
Computer Science · Physics and Astronomy · #Adaptive optics and wavefront sensing #Advanced Vision and Imaging #Cosmology and Nongalactic Astrophysics (astro-ph.CO) #FOS: Physical sciences #Galaxies: Formation, Evolution, Phenomena #Instrumentation and Methods for Astrophysics (astro-ph.IM)

paper · pdf · doi:10.48550/arxiv.1901.08586

openalex publication_date 2019/01/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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 ∼ 24.5, a combined survey analysis increases the effective galaxy number density for cosmic shear studies by ∼ 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 ∼ 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 ∼ 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.

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