Bayesian group finder based on marked point processes. Method and
  feasibility study using the 2MRS data set
  Galaxy groups and clusters are formidable cosmological probes. They permit
the studying of the environmental effects on galaxy formation. A reliable
detection of galaxy groups is an open problem and is important for ongoing and
future cosmological surveys.
  We propose a probabilistic galaxy group detection algorithm based on marked
point processes with interactions.
  The pattern of galaxy groups in a catalogue is seen as a random set of
interacting objects. The positions and the interactions of these objects are
governed by a probability density. The estimator of the unknown cluster pattern
is given by the configuration of objects maximising the proposed probability
density. Adopting the Bayesian framework, the proposed probability density is
maximised using a simulated annealing algorithm. The method provides "for free"
additional information such as the probabilities that a point or two points in
the observation domain belong to the cluster pattern, respectively. These
supplementary tools allow the construction of tests and techniques to validate
and to refine the detection result.
  To test the feasibility of the proposed methodology, we applied it to the
well-studied 2MRS data set. Compared to previously published Friends-of-Friends
(FoF) group finders, the proposed Bayesian group finder gives overall similar
results.
  The proposed Bayesian group finder is tested on a galaxy redshift survey, but
more detailed analyses are needed to understand the actual capabilities of the
algorithm regarding upcoming cosmological surveys. The presented mathematical
framework permits adapting it easily for other data sets (in astronomy and in
other fields of sciences). In cosmology, one promising application is the
detection of galaxy groups in photometric galaxy redshift surveys, while taking
into account the full photometric redshift posteriors. (abridged)
