Morpheus: A Deep Learning Framework For Pixel-Level Analysis of Astronomical Image Data. (arXiv:1906.11248v1 [astro-ph.GA])
<a href="http://arxiv.org/find/astro-ph/1/au:+Hausen_R/0/1/0/all/0/1">Ryan Hausen</a> (UCSC), <a href="http://arxiv.org/find/astro-ph/1/au:+Robertson_B/0/1/0/all/0/1">Brant Robertson</a> (UCSC, IAS)

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 GOODS South field, and
demonstrate a high completeness in recovering known 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.

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 GOODS South field, and
demonstrate a high completeness in recovering known 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.

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