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

arXiv: https://arxiv.org/abs/2201.04714 · HTML: https://mlsanity.com/paper/2201.04714/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 3 references that are in the map.
- First posted on arXiv 12 Jan 2022.

## Built on by (most cited first, 1 of 1)

- [XAMI -- A Benchmark Dataset for Artefact Detection in XMM-Newton Optical Images](https://mlsanity.com/paper/2406.17323/) (2024)

## Abstract (arXiv)

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.