Galaxy Deblending using Residual Dense Neural networks
  We present a new neural network approach for deblending galaxy images in
astronomical data using Residual Dense Neural network (RDN) architecture. We
train the network on synthetic galaxy images similar to the typical
arrangements of field galaxies with a finite point spread function (PSF) and
realistic noise levels. The main novelty of our approach is the usage of two
distinct neural networks: i) a \emph{deblending network} which isolates a
single galaxy postage stamp from the composite and ii) a \emph{classifier
network} which counts the remaining number of galaxies. The deblending proceeds
by iteratively peeling one galaxy at a time from the composite until the image
contains no further objects as determined by classifier, or by other stopping
criteria. By looking at the consistency in the outputs of the two networks, we
can assess the quality of the deblending. We characterize the flux and shape
reconstructions in different quality bins and compare our deblender with the
industry standard, \texttt{SExtractor}. We also discuss possible future
extensions for the project with variable PSFs and noise levels.
