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

## TL;DR

Morpheus is a deep learning framework that performs pixel-level morphological classification of astronomical images, improving source detection and segmentation accuracy using semantic segmentation techniques from computer vision.

## Contribution

It introduces a novel deep learning model that integrates morphological information for pixel-level analysis of astronomical data, enhancing detection and classification resilience.

## Key findings

- High completeness in recovering known sources in Hubble data
- Resilience to false-positive source identifications
- Effective pixel-level morphological classification

## 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.