  We present a fast and efficient hybrid algorithm for selecting exoplanetary
candidates from wide-field transit surveys. Our method is based on the
widely-used SysRem and Box Least-Squares (BLS) algorithms. Patterns of
systematic error that are common to all stars on the frame are mapped and
eliminated using the SysRem algorithm. The remaining systematic errors caused
by spatially localised flat-fielding and other errors are quantified using a
boxcar-smoothing method. We show that the dimensions of the search-parameter
space can be reduced greatly by carrying out an initial BLS search on a coarse
grid of reduced dimensions, followed by Newton-Raphson refinement of the
transit parameters in the vicinity of the most significant solutions. We
illustrate the method's operation by applying it to data from one field of the
SuperWASP survey, comprising 2300 observations of 7840 stars brighter than
V=13.0. We identify 11 likely transit candidates. We reject stars that exhibit
significant ellipsoidal variations indicative of a stellar-mass companion. We
use colours and proper motions from the 2MASS and USNO-B1.0 surveys to estimate
the stellar parameters and the companion radius. We find that two stars showing
unambiguous transit signals pass all these tests, and so qualify for detailed
high-resolution spectroscopic follow-up.
