  In this paper, we investigate the impact of survey strategy on the
performance of self-calibration when the goal is to produce accurate
photometric catalogs from wide-field imaging surveys. This self-calibration
technique utilizes multiple measurements of sources at different focal-plane
positions to constrain instruments' large-scale response (flat-field) from
survey science data alone. We create an artificial sky of sources and
synthetically observe it under four basic survey strategies, creating an
end-to-end simulation of an imaging survey for each. These catalog-level
simulations include realistic measurement uncertainties and a complex
focal-plane dependence of the instrument response. In the self-calibration
step, we simultaneously fit for all the star fluxes and the parameters of a
position-dependent flat-field. For realism, we deliberately fit with a wrong
noise model and a flat-field functional basis that does not include the model
that generated the synthetic data. We demonstrate that with a favorable survey
strategy, a complex instrument response can be precisely self-calibrated. We
show that returning the same sources to very different focal-plane positions is
the key property of any survey strategy designed for accurate retrospective
calibration of this type. The results of this work suggest the following advice
for those considering the design of large-scale imaging surveys: Do not use a
regular, repeated tiling of the sky; instead return the same sources to very
different focal-plane positions.
