Imaging systematics refers to the inhomogeneous distribution of a galaxy sample caused by varying observing conditions and astrophysical foregrounds. Current mitigation methods correct the galaxy density fluctuations $n_{\mathrm{gal}}/{\overline{n}}_{\mathrm{gal}}$ caused by imaging systematics assuming that all galaxies in a sample have the same $n_{\mathrm{gal}}/{\overline{n}}_{\mathrm{gal}}$. Under this assumption, the corrected sample cannot perfectly recover the true correlation function. We name this effect subsample systematics. For a galaxy sample, even if its overall sample statistics [redshift distribution $n(z)$, galaxy bias $b(z)$], are accurately measured, $n(z)$, $b(z)$ can still vary across the observed footprint. It makes the correlation function amplitude of galaxy clustering higher, while correlation functions for galaxy-galaxy lensing and cosmic shear do not have noticeable change. Such a combination could potentially degenerate with physical signals on small angular scales, such as the amplitude of galaxy clustering, the impact of neutrino mass on the matter power spectrum, etc. subsample systematics cannot be corrected using imaging systematics mitigation approaches that rely on the cross-correlation signal between imaging systematics maps and the observed galaxy density field. In this paper, we derive formulated expressions of subsample systematics, demonstrating its fundamental difference with other imaging systematics. We also provide several toy models to visualize this effect. Finally, we discuss a potential method to estimate and mitigate subsample systematics by forward modeling its behavior using synthetic source injection.

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• Cosmology
• Large scale structure of the Universe