# Morpheus: A Deep Learning Framework For Pixel-Level Analysis of Astronomical Image Data

Source: [Morpheus: A Deep Learning Framework For Pixel-Level Analysis of Astronomical Image Data](<https://www.sota2.com/research/paper/68fa3c051e209131ce8d73d0>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T00:54:45.086Z

Authors: Ryan Hausen, Brant Robertson

Published: 2019-06-26T18:00:00.000Z

## Abstract

We present Morpheus, a new model for generating pixel-level morphological classifications of astronomical sources. Morpheus leverages advances in deep learning to perform source detection, source segmentation, and morphological classification pixel-by-pixel via a semantic segmentation algorithm adopted from the field of computer vision. By utilizing morphological information about the flux of real astronomical sources during object detection, Morpheus shows resiliency to false-positive identifications of sources. We evaluate Morpheus by performing source detection, source segmentation, morphological classification on the Hubble Space Telescope data in the five CANDELS fields with a focus on the GOODS South field, and demonstrate a high completeness in recovering known GOODS South 3D-HST sources with H \< 26 AB. We release the code publicly, provide online demonstrations, and present an interactive visualization of the Morpheus results in GOODS South.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1906.11248>)

[PDF](<https://arxiv.org/pdf/1906.11248.pdf>)