Lagrangian Methods Of Cosmic Web Classification

Abstract

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.

arXiv commentsubmitted to MNRAS, comments welcome, references added

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Cited by in corpus (6)

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• Classifying the Large Scale Structure of the Universe with Deep Neural Networks
• The large-scale environment from cosmological simulations I: The baryonic cosmic web
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