English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

In order to study the galaxy population of galaxy clusters with photometricdata one must be able to accurately discriminate between cluster members andnon-members. The redMaPPer cluster finding algorithm treats this problemprobabilistically. Here, we utilize SDSS and GAMA spectroscopic membershiprates to validate the redMaPPer membership probability estimates for clusterswith $z\in[0.1,0.3]$. We find small - but correctable - biases, sourced bythree different systematics. The first two were expected a priori, namely bluecluster galaxies and correlated structure along the line of sight. The thirdsystematic is new: the redMaPPer template fitting exhibits a non-trivialdependence on photometric noise, which biases the original redMaPPerprobabilities when utilizing noisy data. After correcting for these effects, wefind exquisite agreement ($\approx 1\%$) between the photometric probabilityestimates and the spectroscopic membership rates, demonstrating that we canrobustly recover cluster membership estimates from photometric data alone. As abyproduct of our analysis we find that on average unavoidable projectioneffects from correlated structure contribute $\approx 6\%$ of the richness of aredMaPPer galaxy cluster. This work also marks the second public release of theSDSS redMaPPer cluster catalog.

redMaPPer IV: Photometric Membership Identification of Cluster Galaxies with 1% Precision