  Since the start of the Wide Angle Search for Planets (WASP) program, more
than 160 transiting exoplanets have been discovered in the WASP data. In the
past, possible transit-like events identified by the WASP pipeline have been
vetted by human inspection to eliminate false alarms and obvious false
positives. The goal of the present paper is to assess the effectiveness of
machine learning as a fast, automated, and reliable means of performing the
same functions on ground-based wide-field transit-survey data without human
intervention. To this end, we have created training and test datasets made up
of stellar light curves showing a variety of signal types including planetary
transits, eclipsing binaries, variable stars, and non-periodic signals. We use
a combination of machine learning methods including Random Forest Classifiers
(RFCs) and Convolutional Neural Networks (CNNs) to distinguish between the
different types of signals. The final algorithms correctly identify planets in
the test data ~90% of the time, although each method on its own has a
significant fraction of false positives. We find that in practice, a
combination of different methods offers the best approach to identifying the
most promising exoplanet transit candidates in data from WASP, and by extension
similar transit surveys.
