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Workshop: Machine Learning and the Physical Sciences

Statistical Inference for Coadded Astronomical Images

Mallory Wang · Ismael Mendoza · Jeffrey Regier · Camille Avestruz · Cheng Wang


Coadded astronomical images are created by combining 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 novel fully Bayesian approach for performing light source parameter inference on 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.

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