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We present the implementation and use of algorithms for matching point-spreadfunctions (PSFs) within the Pan-STARRS Image Processing Pipeline (IPP).PSF-matching is an essential part of the IPP for the detection of supernovaeand asteroids, but it is also used to homogenize the PSF of inputs to stacks,resulting in improved photometric precision compared to regular coaddition,especially in data with a high masked fraction. We report our experience inconstructing and operating the image subtraction pipeline, and makerecommendations about particular basis functions for constructing thePSF-matching convolution kernel, determining a suitable kernel, parallelisationand quality metrics. We introduce a method for reliably tracking the noise inan image throughout the pipeline, using the combination of a variance map and a`covariance pseudo-matrix'. We demonstrate these algorithms with examples fromboth simulations and actual data from the Pan-STARRS1 telescope.

Pan-STARRS PSF-Matching for Subtraction and Stacking