PyTorchDIA: a flexible, GPU-accelerated numerical approach to Difference Image Analysis

James A Hitchcock, Markus Hundertmark, Daniel Foreman-Mackey, Etienne Bachelet, Martin Dominik, Rachel Street, Yiannis Tsapras

Research output: Contribution to journalArticlepeer-review


We present a GPU-accelerated numerical approach for fast kernel and differential background solutions. The model image proposed in the Bramich (2008) difference image analysis algorithm is analogous to a very simple Convolutional Neural Network (CNN), with a single convolutional filter (i.e. the kernel) and an added scalar bias (i.e. the differential background). Here, we do not solve for the discrete pixel array in the classical, analytical linear least-squares sense. Instead, by making use of PyTorch tensors (GPU compatible multi-dimensional matrices) and associated deep learning tools, we solve for the kernel via an inherently massively parallel optimisation. By casting the Difference Image Analysis (DIA) problem as a GPU-accelerated optimisation which utilises automatic differentiation tools, our algorithm is both flexible to the choice of scalar objective function, and can perform DIA on astronomical data sets at least an order of magnitude faster than its classical analogue. More generally, we demonstrate that tools developed for machine learning can be used to address generic data analysis and modelling problems.
Original languageEnglish
Pages (from-to)3561–3579
Number of pages20
JournalMonthly Notices of the Royal Astronomical Society
Issue number3
Early online date21 Apr 2021
Publication statusPublished - Jul 2021


  • Methods: data analysis
  • Techniques: image processing
  • Software: development


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