  Astronomical source deblending is the process of separating the contribution
of individual stars or galaxies (sources) to an image comprised of multiple,
possibly overlapping sources. Astronomical sources display a wide range of
sizes and brightnesses and may show substantial overlap in images. Astronomical
imaging data can further challenge off-the-shelf computer vision algorithms
owing to its high dynamic range, low signal-to-noise ratio, and unconventional
image format. These challenges make source deblending an open area of
astronomical research, and in this work, we introduce a new approach called
Partial-Attribution Instance Segmentation that enables source detection and
deblending in a manner tractable for deep learning models. We provide a novel
neural network implementation as a demonstration of the method.
