  Most existing star-galaxy classifiers use the reduced summary information
from catalogs, requiring careful feature extraction and selection. The latest
advances in machine learning that use deep convolutional neural networks allow
a machine to automatically learn the features directly from data, minimizing
the need for input from human experts. We present a star-galaxy classification
framework that uses deep convolutional neural networks (ConvNets) directly on
the reduced, calibrated pixel values. Using data from the Sloan Digital Sky
Survey (SDSS) and the Canada-France-Hawaii Telescope Lensing Survey (CFHTLenS),
we demonstrate that ConvNets are able to produce accurate and well-calibrated
probabilistic classifications that are competitive with conventional machine
learning techniques. Future advances in deep learning may bring more success
with current and forthcoming photometric surveys, such as the Dark Energy
Survey (DES) and the Large Synoptic Survey Telescope (LSST), because deep
neural networks require very little, manual feature engineering.
