We plan to perform systematic searches in the data of the Hyper Suprime-Cam - Subaru Strategic Program (HSC-SSP) for clusters and protoclusters of galaxies at 0 < z ~< 2. For this, we will estimate photometric redshifts using deep learning techniques and will evaluate the spatial distribution of galaxies through density estimators in different redshift slices up to z ~ 2, in order to identify significant overdensities. To reach this upper limit in redshift, we will use the infra-red W1 and W2 bands available in the unWISE catalog (Schlafly et al. 2018). We will use realistic mocks obtained with PCcones (Araya-Araya et al., 2021) to assess the efficiency of a selection based on colors (e.g. red sequence galaxies) and luminosities. We propose identifying the brightest cluster galaxy (BCG) with a Bayesian probabilistic approach. By using mocks emulating the survey, as well as observations from other surveys, we will establish additional probabilistic procedures to determine the member galaxies, cluster center, mean redshift, and richness of the structure. The mocks, as well as other studies will allow us to evaluate our cluster identification completeness and purity. This project expands and improves the tools implemented in Vicentin et al. 2021 and Araya-Araya et al. 2021, and the same 6 CFHT images used in the former paper will be used in this project for tests and validation of the algorithm. The cluster sample obtained by our cluster finder in the HSC area will add to the effort of target selection for the Prime Focus Spectrograph (PFS) Subaru Strategic Program}, which will start in 2023.
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