  The aim of the work presented in this paper is to test and optimise supernova
detection methods based on the optimal image subtraction technique. The main
focus is on applying the detection methods to wide field supernova imaging
surveys and in particular to the Stockholm VIMOS Supernova Survey (SVISS). We
have constructed a supernova detection pipeline for imaging surveys. The core
of the pipeline is image subtraction using the ISIS 2.2 package. Using real
data from the SVISS we simulate supernovae in the images, both inside and
outside galaxies. The detection pipeline is then run on the simulated frames
and the effects of image quality and subtraction parameters on the detection
efficiency and photometric accuracy are studied. The pipeline allows efficient
detection of faint supernovae in the deep imaging data. It also allows
controlling and correcting for possible systematic effects in the SN detection
and photometry. We find such a systematic effect in the form of a small
systematic flux offset remaining at the positions of galaxies in the subtracted
frames. This offset will not only affect the photometric accuracy of the
survey, but also the detection efficiencies. Our study has shown that ISIS 2.2
works well for the SVISS data. We have found that the detection efficiency and
photometric accuracy of the survey are affected by the stamp selection for the
image subtraction and by host galaxy brightness. With our tools the subtraction
results can be further optimised, any systematic effects can be controlled and
photometric errors estimated, which is very important for the SVISS, as well as
for future SN searches based on large imaging surveys such as Pan-STARRS and
LSST.
