DAWIS: a detection algorithm with wavelets for intracluster light studies

Large amounts of deep optical images will be available in the near future, allowing statistically significant studies low surface brightness structures such as intracluster light (ICL) galaxy clusters. The detection these requires efficient algorithms dedicated to this task, where traditional methods suffer difficulties. We present our new Detection Algorithm with Wavelets for Intracluster Studies (DAWIS), developed and optimised sources images, particular (but not limited to) ICL. DAWIS follows a multiresolution vision based on wavelet representation detect sources, embedded an iterative procedure called synthesis-by-analysis approach restore complete unmasked distribution very good quality. algorithm is built so can classified criteria depending analysis goal; we display work case ICL measurement fractions. test efficiency 270 mock clusters various profiles compare its more threshold method. also run real cluster image, output results obtained previous multiscale algorithms. find simulations that average able disentangle from efficiently, greater quantity flux due way it handles sky background noise. show fraction, metric used regular basis characterise ICL, subject several biases both galaxies fluxes. In detects faint extended source absolute magnitude two orders brighter than methods.