Artificial Intelligence for source deblending in the next generation of astrophysical big data imaging surveys – Combining Euclid and LSST

Project Summary

Astronomy, as many other disciplines, is entering a big data era. The next generation of space (e.g. Euclid) and ground based (e.g. LSST) imaging survey will produce images of billions of galaxies at unprecedented depth. This new regime requires revisiting the existing methods to process the data both in terms of speed and accuracy in order for these surveys to achieve the main scientific goals. A particularly important source of bias is the one caused by overlapping sources (or blending) in the 2D projected plane of the sky. Galaxies which are at very different distances end up blended together. Built on previous synergic experiences of two research groups, this project explores the use of state-of-the art Artificial Intelligence techniques for deblending of galaxy images. We will in particular explore synergies between LSST and Euclid.

Marc Huertas-Company