Galaxy cluster detection and characterization in the S-PLUS and miniJPAS surveys скачать в хорошем качестве

Galaxy cluster detection and characterization in the S-PLUS and miniJPAS surveys

Galaxy cluster detection and characterization in the S-PLUS and miniJPAS surveys Lia Doubrawa IAG, BR Galaxy clusters are vital tools for exploring and comprehending the Universe. This presentation shows findings from the S-PLUS and miniJPAS photometric surveys. We use the density-based PZWav algorithm to identify galaxy cluster candidates and introduce an adaptive membership estimator (AME) to assign probabilistic memberships to galaxies near cluster centers. Our study involves a comparison with tailored simulations that reproduce S-PLUS/DR1, demonstrating our catalog's 80% purity and 95% completeness for clusters with richness greater than 4. For miniJPAS, considering an overlapping X-ray catalog, we determine cluster masses, revealing structures of 4 x 10^13 M☉ at redshift 0.75, highlighting the survey's depth. This investigation emphasizes the potential of S-PLUS and J-PAS surveys, along with our applied methodologies. Lia Doubrawa is currently a PhD student at Instituto de Astronomia, Geofísica e Ciências Atmosféricas da Universidade de São Paulo (IAG/USP). Has a master's degree in Physics with emphasis on Astronomy from the Universidade Tecnológica Federal do Paraná (UTFPR, 2019) and a bachelor's degree in Physics from the Universidade Federal do Paraná (UFPR, 2017). Has experience in numerical simulations of merging galaxy clusters. Currently she works with galaxy cluster detection and characterization using probabilistic membership of galaxies in photometric surveys.