Kavli Affiliate: Eric Charles
| First 5 Authors: Qianjun Hang, Benjamin Joachimi, Eric Charles, John Franklin Crenshaw, Patricia Larsen
| Summary:
We investigate the impact of spatial survey non-uniformity on the galaxy
redshift distributions for forthcoming data releases of the Rubin Observatory
Legacy Survey of Space and Time (LSST). Specifically, we construct a mock
photometry dataset degraded by the Rubin OpSim observing conditions, and
estimate photometric redshifts of the sample using a template-fitting photo-$z$
estimator, BPZ, and a machine learning method, FlexZBoost. We select the Gold
sample, defined as $i<25.3$ for 10 year LSST data, with an adjusted magnitude
cut for each year and divide it into five tomographic redshift bins for the
weak lensing lens and source samples. We quantify the change in the number of
objects, mean redshift, and width of each tomographic bin as a function of the
coadd $i$-band depth for 1-year (Y1), 3-year (Y3), and 5-year (Y5) data. In
particular, Y3 and Y5 have large non-uniformity due to the rolling cadence of
LSST, hence provide a worst-case scenario of the impact from non-uniformity. We
find that these quantities typically increase with depth, and the variation can
be $10-40%$ at extreme depth values. Using Y3 as an example, we propagate the
variable depth effect to the weak lensing $3times2$pt analysis, and assess the
impact on cosmological parameters via a Fisher forecast. We find that galaxy
clustering is most susceptible to variable depth, and non-uniformity needs to
be mitigated below $3%$ to recover unbiased cosmological constraints. There is
little impact on galaxy-shear and shear-shear power spectra, given the expected
LSST Y3 noise.
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