AMICO-WL: an algorithm for weak-lensing detection of galaxy clusters

We developed a highly optimized routine for the detection of clusters of galaxies: AMICO-WL, which implements an improved version of the optimal linear matched filter presented in Maturi et al. 2005 inside the computational environment of AMICO (Bellagamba et al., 2018). The filter is optimized to maximize the SNR of the detections and minimize the number of spurious detections caused by the superposition of large-scale structures. Our goal is to detect the matter distribution of clusters, represented mainly by their dark matter halos, through their weak lensing signatures by applying the filter to a catalog of simulated galaxy ellipticities. The data are mocks of Euclid's expected data we thus also provide a forecast for the filter performances on weak lensing for this mission. We studied the sample's completeness and purity by matching the detections with the halos in the simulations. We found SNR∼2.5 as a reliable threshold for the detection, in fact the purity is 70% against the expected 80%. We studied the completeness in the redshift-mass plane of the halos. The completeness reaches almost 100% in the case of the halos with virial mass log10(M200/M⊙/h) > 14.4 and redshift in the range [0.2,0.4]. As expected we reach the higher completeness where the efficiency of the gravitational lensing effect is maximum, i.e where the lens is located in between the sources and the observer. We refined the strategy by using only the galaxies with z>0.6 to remove most of the non-lensed foreground sources then compared the results with the analysis performed with the full galaxy catalog. Using the truncated dataset we detected 607 more objects and consequent higher completeness, 47% against 45% for a fixed 65% purity in both catalogs. The ultimate goal is to build a routine for the weak-lensing detection of clusters that will work in combination with the photometric detection algorithm implemented in AMICO for the analysis of wide-field surveys (KiDS,CHFTLens,DES,LSST,Euclid)