  Coadded astronomical images are created by stacking multiple single-exposure
images. Because coadded images are smaller in terms of data size than the
single-exposure images they summarize, loading and processing them is less
computationally expensive. However, image coaddition introduces additional
dependence among pixels, which complicates principled statistical analysis of
them. We present a principled Bayesian approach for performing light source
parameter inference with coadded astronomical images. Our method implicitly
marginalizes over the single-exposure pixel intensities that contribute to the
coadded images, giving it the computational efficiency necessary to scale to
next-generation astronomical surveys. As a proof of concept, we show that our
method for estimating the locations and fluxes of stars using simulated coadds
outperforms a method trained on single-exposure images.
