A group finder algorithm optimised for the study of local galaxy environment. (arXiv:1910.05135v1 [astro-ph.GA])
<a href="http://arxiv.org/find/astro-ph/1/au:+Graham_M/0/1/0/all/0/1">Mark T. Graham</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Cappellari_M/0/1/0/all/0/1">Michele Cappellari</a>
The majority of galaxy group catalogues available in the literature use the
popular friends-of-friends algorithm which links galaxies using a linking
length. One potential drawback to this approach is that clusters of point can
be link with thin bridges which may not be desirable. Furthermore, these
algorithms are designed with large-scales galaxy surveys in mind rather than
small-scale, local galaxy environments, where attention to detail is important.
Here we present a new simple group finder algorithm, TD-ENCLOSER, that finds
the group that encloses a target galaxy of interest. TD-ENCLOSER is based on
the kernel density estimation method which treats each galaxy, represented by a
zero-dimensional particle, as a two-dimensional circular Gaussian. The
algorithm assigns galaxies to peaks in the density field in order of density in
descending order (“Top Down”) so that galaxy groups “grow” around the density
peaks. Outliers in under-dense regions are prevented from joining groups by a
specified hard threshold, while outliers at the group edges are clipped below a
soft (blurred) interior density level. The group assignments are largely
insensitive to all free parameters apart from the hard density threshold and
the kernel standard deviation, although this is a known feature of
density-based group finder algorithms, and operates with a computing speed that
increases linearly with the size of the input sample. In preparation for a
companion paper, we also present a simple algorithm to select unique
representative groups when duplicates occur. TD-ENCLOSER produces results
comparable to those from a widely used catalogue, as shown in a companion
paper. A smoothing scale of 0.3 Mpc provides the most realistic group
structure.
The majority of galaxy group catalogues available in the literature use the
popular friends-of-friends algorithm which links galaxies using a linking
length. One potential drawback to this approach is that clusters of point can
be link with thin bridges which may not be desirable. Furthermore, these
algorithms are designed with large-scales galaxy surveys in mind rather than
small-scale, local galaxy environments, where attention to detail is important.
Here we present a new simple group finder algorithm, TD-ENCLOSER, that finds
the group that encloses a target galaxy of interest. TD-ENCLOSER is based on
the kernel density estimation method which treats each galaxy, represented by a
zero-dimensional particle, as a two-dimensional circular Gaussian. The
algorithm assigns galaxies to peaks in the density field in order of density in
descending order (“Top Down”) so that galaxy groups “grow” around the density
peaks. Outliers in under-dense regions are prevented from joining groups by a
specified hard threshold, while outliers at the group edges are clipped below a
soft (blurred) interior density level. The group assignments are largely
insensitive to all free parameters apart from the hard density threshold and
the kernel standard deviation, although this is a known feature of
density-based group finder algorithms, and operates with a computing speed that
increases linearly with the size of the input sample. In preparation for a
companion paper, we also present a simple algorithm to select unique
representative groups when duplicates occur. TD-ENCLOSER produces results
comparable to those from a widely used catalogue, as shown in a companion
paper. A smoothing scale of 0.3 Mpc provides the most realistic group
structure.
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