Speaker
Dr
Fred Moolekamp
Description
It is estimated that at least 63% of sources observed in the 10 year LSST survey will have at least 2% of their flux blended with another object. Achieving many of the LSST science goals requires a working deblender to separate the flux from overlapping stars and galaxies by extracting morphological and spectral data for each source. The primary focus of this talk will be on scarlet, the python package I have been developing with Peter Melchior that will soon be implemented in the HSC and LSST software pipelines. Time permitted I will also mention more ambitious efforts by other members of the Dark Energy Science Collaboration (DESC) using neural networks to solve the blending problem.
