Partial-Attribution Instance Segmentation for Astronomical Source Detection and Deblending

• URL: http://arxiv.org/abs/2201.04714v1
• Date: Wed, 12 Jan 2022 21:59:13 GMT
• Title: Partial-Attribution Instance Segmentation for Astronomical Source Detection and Deblending
• Authors: Ryan Hausen, Brant Robertson
• Abstract summary: We introduce a new approach called Partial-Attribution Instances 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.
• Score: 0.24920602678297968
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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