http://arxiv.org/abs/2211.09300
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.
M. Wang, I. Mendoza, C. Wang, et. al.
Fri, 18 Nov 22
50/70
Comments: Accepted to the NeurIPS 2022 Machine Learning and the Physical Sciences workshop. 6 pages, 2 figures