Morpheus: A Deep Learning Framework For Pixel-Level Analysis of Astronomical Image Data
This work addresses the need for accurate source detection and classification in astronomy, though it is incremental as it adapts existing computer vision methods to this domain.
The authors tackled the problem of pixel-level morphological classification of astronomical sources by developing Morpheus, a deep learning framework that achieved high completeness in recovering known sources from Hubble Space Telescope data, specifically with H < 26 AB in the GOODS South field.
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.