Probabilistic segmentation of overlapping galaxies for large cosmological surveys

Understand

Encoder-Decoder networks such as U-Nets have been applied successfully in a wide range of computer vision tasks, especially for image segmentation of different flavours across different fields.

• Nevertheless, most applications lack of a satisfying quantification of the uncertainty of the prediction.
• Yet, a well calibrated segmentation uncertainty can be a key element for scientific applications such as precision cosmology.
• In this on-going work, we explore the use of the probabilistic version of the U-Net, recently proposed by Kohl et al (2018), and adapt it to automate the segmentation of galaxies for large photometric surveys.