Partial-Attribution Instance Segmentation for Astronomical Source
  Detection and Deblending
  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.
