Lagrangian Methods Of Cosmic Web Classification

TL;DR

This paper introduces a Lagrangian method for cosmic web classification that classifies individual objects based on their local environment, reducing computational costs and enabling direct application to observed galaxy samples.

Contribution

It presents a novel Lagrangian approach using smoothed particle hydrodynamics techniques for cosmic web classification, improving efficiency and applicability over traditional grid-based methods.

Findings

Lagrangian method classifies individual objects efficiently.

Reduces computational cost compared to grid-based methods.

Allows direct application to observed galaxy samples.

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.