Lagrangian Methods Of Cosmic Web Classification
  The cosmic web defines the large scale distribution of matter we see in the
Universe today. Classifying the cosmic web into voids, sheets, filaments and
nodes allows one to explore structure formation and the role environmental
factors have on halo and galaxy properties. While existing studies of cosmic
web classification concentrate on grid based methods, this work explores a
Lagrangian approach where the V-web algorithm proposed by Hoffman et al. (2012)
is implemented with techniques borrowed from smoothed particle hydrodynamics.
The Lagrangian approach allows one to classify individual objects (e.g.
particles or halos) based on properties of their nearest neighbours in an
adaptive manner. It can be applied directly to a halo sample which dramatically
reduces computational cost and potentially allows an application of this
classification scheme to observed galaxy samples. Finally, the Lagrangian
nature admits a straight forward inclusion of the Hubble flow negating the
necessity of a visually defined threshold value which is commonly employed by
grid based classification methods.
