We present a technique to measure lightcurves of time-variable point sources
on a spatially structured background from imaging data. The technique was
developed to measure light curves of SNLS supernovae in order to infer their
distances. This photometry technique performs simultaneous PSF photometry at
the same sky position on an image series. We describe two implementations of
the method: one that resamples images before measuring fluxes, and one which
does not. In both instances, we sketch the key algorithms involved and present
the validation using semi-artificial sources introduced in real images in order
to assess the accuracy of the supernova flux measurements relative to that of
surrounding stars. We describe the methods required to anchor these PSF fluxes
to calibrated aperture catalogs, in order to derive SN magnitudes. We find a
marginally significant bias of 2 mmag of the after-resampling method, and no
bias at the mmag accuracy for the non-resampling method. Given surrounding star
magnitudes, we determine the systematic uncertainty of SN magnitudes to be less
than 1.5 mmag, which represents about one third of the current photometric
calibration uncertainty affecting SN measurements. The SN photometry delivers
several by-products: bright star PSF flux mea- surements which have a
repeatability of about 0.6%, as for aperture measurements; we measure relative
astrometric positions with a noise floor of 2.4 mas for a single-image bright
star measurement; we show that in all bands of the MegaCam instrument, stars
exhibit a profile linearly broadening with flux by about 0.5% over the whole
brightness range.Comment: Accepted for publication in A&A. 20 page