  Galaxy-scale strong gravitational lensing is not only a valuable probe of the
dark matter distribution of massive galaxies, but can also provide valuable
cosmological constraints, either by studying the population of strong lenses or
by measuring time delays in lensed quasars. Due to the rarity of galaxy-scale
strongly lensed systems, fast and reliable automated lens finding methods will
be essential in the era of large surveys such as LSST, Euclid, and WFIRST. To
tackle this challenge, we introduce CMU DeepLens, a new fully automated
galaxy-galaxy lens finding method based on Deep Learning. This supervised
machine learning approach does not require any tuning after the training step
which only requires realistic image simulations of strongly lensed systems. We
train and validate our model on a set of 20,000 LSST-like mock observations
including a range of lensed systems of various sizes and signal-to-noise ratios
(S/N). We find on our simulated data set that for a rejection rate of
non-lenses of 99%, a completeness of 90% can be achieved for lenses with
Einstein radii larger than 1.4" and S/N larger than 20 on individual $g$-band
LSST exposures. Finally, we emphasize the importance of realistically complex
simulations for training such machine learning methods by demonstrating that
the performance of models of significantly different complexities cannot be
distinguished on simpler simulations. We make our code publicly available at
https://github.com/McWilliamsCenter/CMUDeepLens .
