We present supernova rate measurements at redshift 0.1-1.0 from the Stockholm VIMOS Supernova Survey (SVISS). The sample contains 16 supernovae in total. The discovered supernovae have been classified as core collapse or type Ia supernovae (9 and 7, respectively) based on their light curves, colour evolution and host galaxy photometric redshift. at z = 0.62. All of these rate estimates have been corrected for host galaxy extinction, using a method that includes supernovae missed in infrared bright galaxies at high redshift. We use Monte Carlo simulations to make a thorough study of the systematic effects from assumptions made when calculating the rates and find that the most important errors come from misclassification, the assumed mix of faint and bright supernova types and uncertainties in the extinction correction. We compare our rates to other observations and to the predicted rates for core collapse and type Ia supernovae based on the star formation history and different models of the delay time distribution. Overall, our measurements, when taking the effects of extinction into account, agree quite well with the predictions and earlier results. Our results highlight the importance of understanding the role of systematic effects, and dust extinction in particular, when trying to estimate the rates of supernovae at moderate to high redshift.

Aims. 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). Methods. 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. Results. 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. Conclusions. 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.