Euclid: Galaxy cluster detection through the weak lensing effect - algorithm assessment and selection

Weak gravitational lensing offers a powerful way to detect galaxy clusters by directly tracing their total matter content. Its effectiveness increases with the density of background galaxies, a requirement that is now being met by the high sensitivity and wide coverage of current large-area surveys. A prime example is the Euclid telescope, which will map approximately 14 000 deg2 of the sky, measuring the shapes of billions of galaxies and opening a meaningful window for detecting galaxy clusters uniquely through their weak lensing signal. We present the results of nine galaxy cluster detection algorithms in a blind challenge, using 1200 deg2 of Euclid-like weak lensing observations from the DEMNUni-Cov simulations. The performance of these methods was assessed by matching their detections to known synthetic clusters with signal-to-noise ratios greater than 2, satisfying the z-M selection cut, and adopting two different matching procedures. The purpose of the challenge was to identify and improve strategies for galaxy cluster detection via weak lensing for the upcoming Euclid data releases. We pre-selected four methods based on their individual performance and complementarity. Each pre-selected method adopts a distinct approach: AMICO-WL uses an optimal filtering technique, DoG employs Gaussian filtering, O21 relies on aperture-mass filtering, and W234 applies multi-scale wavelet filtering. Together, the results of these methods can be merged to enhance the processing of Euclid data. Individually, these algorithms reach approximately 10% completeness for a mean purity of 90%, while their combination leads to roughly a two-fold improvement in overall performance, exceeding 70% completeness for low-redshift, high-mass clusters. When extrapolating the results of this work to Euclid Data Release 1, we expect to detect approximately 2500 galaxy clusters via weak gravitational lensing.