  In the last decade, over a million stars were monitored to detect transiting
planets. Manual interpretation of potential exoplanet candidates is labor
intensive and subject to human error, the results of which are difficult to
quantify. Here we present a new method of detecting exoplanet candidates in
large planetary search projects which, unlike current methods uses a neural
network. Neural networks, also called "deep learning" or "deep nets" are
designed to give a computer perception into a specific problem by training it
to recognize patterns. Unlike past transit detection algorithms deep nets learn
to recognize planet features instead of relying on hand-coded metrics that
humans perceive as the most representative. Our convolutional neural network is
capable of detecting Earth-like exoplanets in noisy time-series data with a
greater accuracy than a least-squares method. Deep nets are highly
generalizable allowing data to be evaluated from different time series after
interpolation without compromising performance. As validated by our deep net
analysis of Kepler light curves, we detect periodic transits consistent with
the true period without any model fitting. Our study indicates that machine
learning will facilitate the characterization of exoplanets in future analysis
of large astronomy data sets.
