A method for optimal image subtraction

We present a new method designed for optimal subtraction of two images with different seeing. Using image subtraction appears to be essential for full analysis of microlensing survey images; however, a perfect subtraction of two images is not easy, as it requires the derivation of an extremely accurate convolution kernel. Some empirical attempts to find the kernel have used a Fourier transform of bright stars, but solving the statistical problem of finding the best kernel solution has never really been tackled. We demonstrate that it is possible to derive an optimal kernel solution from a simple least-squares analysis using all the pixels of both images, and we also show that it is possible to fit the differential background variation at the same time. We show that point-spread function (PSF) variations can be easily handled by the method. To demonstrate the practical efficiency of the method, we analyzed some images from a Galactic Bulge field monitored by the OGLE II project. We find that the residuals in the subtracted images are very close to the photon noise expectations. We also present some light curves of variable stars and show that despite high crowding levels, we get an error distribution close to that expected from photon noise alone. We thus demonstrate that nearly optimal differential photometry can be achieved even in very crowded fields. We suggest that this algorithm might be particularly important for microlensing surveys, where the photometric accuracy and completeness levels could be very significantly improved by using this method.