AMICO galaxy clusters in KiDS-1000: cosmological sample - Maturi, Matteo; Radovich, M.; Moscardini, L.; Lesci, G. F.; Castignani, G.; Marulli, F.; Puddu, E. A.; Romanello, M.; Sereno, M.; Giocoli, C.; Ingoglia, L.; Bardelli, S.; et al. (Astronomy and astrophysics, 2025)
Context: Galaxy clusters provide key insights into cosmic structure formation and galaxy formation, and they are essential for cosmological studies. Aims: We present a catalog of galaxy clusters detected in the Kilo-Degree Survey (KiDS-DR4) optimized for cosmological analyses and investigations of cluster properties. Each detection includes probabilistic membership assignments for the KiDS-DR4 galaxies within the magnitude range 15 < r′< 24. Methods: Using the Adaptive Matched Identifier of Clustered Objects (AMICO) algorithm, we identified 23 965 clusters over an effective area of about 839 deg 2 in the redshift range 0.1 ≤ z ≤ 0.9, with a signal-to-noise ratio of S/N > 3.5. The sample is highly homogeneous across the entire survey thanks to the restrictive galaxy selection criteria we adopted. Spectroscopic data from the GAMA survey were used to calibrate the photometric redshift of the clusters and assess their uncertainties. We introduced algorithmic enhancements to AMICO to mitigate border effects among neighbor tiles. Quality flags are also provided for each cluster detection. The sample purity and completeness assessments were estimated using the SINFONIA data driven approach, thus avoiding strong assumptions embedded in numerical simulations. We introduced a blinding scheme of the selection function that is intended to support the cosmological analyses. Results: Our cluster sample includes 321 cross-matches with the X-ray eRASS1 “primary” sample and 235 matches with the ACT-DR5 cluster sample. We derived a mass-proxy scaling relation based on intrinsic richness, λ*, using masses from the eRASS1 catalog. Conclusions: The KiDS-DR4 cluster catalog provides a valuable dataset for investigating galaxy cluster properties and contributes to cosmological studies by offering a large, well-characterized cluster sample.
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