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Large amounts of deep optical images will be available in the near future,allowing statistically significant studies of low surface brightness structuressuch as intracluster light (ICL) in galaxy clusters. The detection of thesestructures requires efficient algorithms dedicated to this task, wheretraditional methods suffer difficulties. We present our new Detection Algorithmwith Wavelets for Intracluster light Studies (DAWIS), developed and optimisedfor the detection of low surface brightness sources in images, in particular(but not limited to) ICL. DAWIS follows a multiresolution vision based onwavelet representation to detect sources, embedded in an iterative procedurecalled synthesis-by-analysis approach to restore the complete unmasked lightdistribution of these sources with very good quality. The algorithm is built sosources can be classified based on criteria depending on the analysis goal; wedisplay in this work the case of ICL detection and the measurement of ICLfractions. We test the efficiency of DAWIS on 270 mock images of galaxyclusters with various ICL profiles and compare its efficiency to moretraditional ICL detection methods such as the surface brightness thresholdmethod. We also run DAWIS on a real galaxy cluster image, and compare theoutput to results obtained with previous multiscale analysis algorithms. Wefind in simulations that in average DAWIS is able to disentangle galaxy lightfrom ICL more efficiently, and to detect a greater quantity of ICL flux due tothe way it handles sky background noise. We also show that the ICL fraction, ametric used on a regular basis to characterise ICL, is subject to severalmeasurement biases both on galaxies and ICL fluxes. In the real galaxy clusterimage, DAWIS detects a faint and extended source with an absolute magnitude twoorders brighter than previous multiscale methods.

DAWIS, a Detection Algorithm with Wavelets for Intracluster light Studies