DAWIS: a detection algorithm with wavelets for intracluster light studies

Abstract

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

On the Origin and Evolution of the Intra-Cluster Light: A Brief Review of the Most Recent Developments

“…It is worth mentioning another approach for detecting the ICL that has recently been having some success, a method that makes use of multiscale, wavelet-based algorithms. There are several examples of this approach that have been used in recent years (e.g., [105][106][107][108][109]). One of the latest, just to quote an example, is the code called DAWIS ( [109] and references therein).…”

“…There are several examples of this approach that have been used in recent years (e.g., [105][106][107][108][109]). One of the latest, just to quote an example, is the code called DAWIS ( [109] and references therein). DAWIS is an algorithm, based on wavelet representation, built to restore the unmasked light distribution of given sources as much as possible.…”

“…DAWIS is an algorithm, based on wavelet representation, built to restore the unmasked light distribution of given sources as much as possible. Ellien et al [109] compared the performance of DAWIS with the more common methods described above and found that it can separate the ICL from other sources more efficiently (in the sense that DAWIS performs better) than other methods and is also able to recover a larger quantity of ICL given the way it treats the sky background noise. For readers interested in the details of DAWIS and similar former algorithms of the same family, I refer them to Ellien et al [109] and references therein.…”

“…Ellien et al [109] compared the performance of DAWIS with the more common methods described above and found that it can separate the ICL from other sources more efficiently (in the sense that DAWIS performs better) than other methods and is also able to recover a larger quantity of ICL given the way it treats the sky background noise. For readers interested in the details of DAWIS and similar former algorithms of the same family, I refer them to Ellien et al [109] and references therein. The ICL fraction as a function of distance from the center for different surface brightness cuts and different ICL measurements methods.…”

“…There is no general agreement of a possible dependence of f ICL on cluster mass among different studies, both observational and theoretical ( [4,10,14,16,17,24,31,33,49,53,54,60,78,96,97,109,110,[128][129][130][131] just to quote some theoretical and the most recent observational studies). Some theoretical works (e.g., [49,55,130,132]) find an increasing f ICL with cluster mass, others (e.g., [4,18,20]) no relation, and others (e.g., [14]) a slight decreasing f ICL with increasing cluster mass.…”