Deblending and Classifying Astronomical Sources with Mask R-CNN Deep
  Learning
  We apply a new deep learning technique to detect, classify, and deblend
sources in multi-band astronomical images. We train and evaluate the
performance of an artificial neural network built on the Mask R-CNN image
processing framework, a general code for efficient object detection,
classification, and instance segmentation. After evaluating the performance of
our network against simulated ground truth images for star and galaxy classes,
we find a precision of 92% at 80% recall for stars and a precision of 98% at
80% recall for galaxies in a typical field with $\sim30$ galaxies/arcmin$^2$.
We investigate the deblending capability of our code, and find that clean
deblends are handled robustly during object masking, even for significantly
blended sources. This technique, or extensions using similar network
architectures, may be applied to current and future deep imaging surveys such
as LSST and WFIRST. Our code, Astro R-CNN, is publicly available at
https://github.com/burke86/astro_rcnn.
