Kavli Affiliate: Chihway Chang
| First 5 Authors: Benjamin Levine, Javier Sánchez, Chihway Chang, Anja von der Linden, Eboni Collins
| Summary:
The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) will
survey the southern sky to create the largest galaxy catalog to date, and its
statistical power demands an improved understanding of systematic effects such
as source overlaps, also known as blending. In this work we study how blending
introduces a bias in the number counts of galaxies (instead of the flux and
colors), and how it propagates into galaxy clustering statistics. We use the
$300,$deg$^2$ DC2 image simulation and its resulting galaxy catalog (LSST Dark
Energy Science Collaboration et al. 2021) to carry out this study. We find
that, for a LSST Year 1 (Y1)-like cosmological analyses, the number count bias
due to blending leads to small but statistically significant differences in
mean redshift measurements when comparing an observed sample to an unblended
calibration sample. In the two-point correlation function, blending causes
differences greater than 3$sigma$ on scales below approximately $10’$, but
large scales are unaffected. We fit $Omega_{rm m}$ and linear galaxy bias in
a Bayesian cosmological analysis and find that the recovered parameters from
this limited area sample, with the LSST Y1 scale cuts, are largely unaffected
by blending. Our main results hold when considering photometric redshift and a
LSST Year 5 (Y5)-like sample.
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