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 Plus annual

Presents the access of premium content as premium feature

Premium Content

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 annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Galaxy groups and clusters are formidable cosmological probes. They permitthe studying of the environmental effects on galaxy formation. A reliabledetection of galaxy groups is an open problem and is important for ongoing andfuture cosmological surveys.  We propose a probabilistic galaxy group detection algorithm based on markedpoint processes with interactions.  The pattern of galaxy groups in a catalogue is seen as a random set ofinteracting objects. The positions and the interactions of these objects aregoverned by a probability density. The estimator of the unknown cluster patternis given by the configuration of objects maximising the proposed probabilitydensity. Adopting the Bayesian framework, the proposed probability density ismaximised using a simulated annealing algorithm. The method provides "for free"additional information such as the probabilities that a point or two points inthe observation domain belong to the cluster pattern, respectively. Thesesupplementary tools allow the construction of tests and techniques to validateand to refine the detection result.  To test the feasibility of the proposed methodology, we applied it to thewell-studied 2MRS data set. Compared to previously published Friends-of-Friends(FoF) group finders, the proposed Bayesian group finder gives overall similarresults.  The proposed Bayesian group finder is tested on a galaxy redshift survey, butmore detailed analyses are needed to understand the actual capabilities of thealgorithm regarding upcoming cosmological surveys. The presented mathematicalframework permits adapting it easily for other data sets (in astronomy and inother fields of sciences). In cosmology, one promising application is thedetection of galaxy groups in photometric galaxy redshift surveys, while takinginto account the full photometric redshift posteriors. (abridged)

Bayesian group finder based on marked point processes. Method and feasibility study using the 2MRS data set