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.
@article{arxiv.1906.11248,
title = {Morpheus: A Deep Learning Framework For Pixel-Level Analysis of Astronomical Image Data},
author = {Ryan Hausen and Brant Robertson},
journal= {arXiv preprint arXiv:1906.11248},
year = {2020}
}
Comments
47 pages, 33 figures. Version accepted by ApJS. More information about Morpheus is available at https://morpheus-project.github.io/morpheus/