COWS: A filament finder for Hessian cosmic web identifiers

TL;DR

The paper introduces COWS, a new filament finder for the cosmic web based on Hessian matrix analysis, which identifies discrete filaments with well-defined endpoints and properties, and is validated against various parameters.

Contribution

COWS is a novel filament identification method that segments filaments from the V-web using a thinning algorithm, providing well-defined structures and publicly available code.

Findings

Filaments sit at local density ridges and align with velocity fields

The method accurately recovers filament length and density profiles

Results are robust against resolution and threshold variations

Abstract

The large scale galaxy and matter distribution is often described by means of the cosmic web made up of voids, sheets, filaments and knots. Many different recipes exist for identifying this cosmic web. Here we focus on a sub-class of cosmic web identifiers, based on the analysis of the Hessian matrix, and proposed a method, called COsmic Web Skeleton (COWS), of separating a set of filaments cells into an ensemble of individual discreet filaments. Specifically, a thinning algorithm is applied to velocity shear tensor based cosmic web (V-web) to identify the spine of the filaments. This results in a set of filaments with well defined end-point and length. It is confirmed that these sit at local density ridges and align with the appropriate direction defined by the underlying velocity field. The radial density profile of these curved cylindrical filaments, as well as the distribution of their lengths is also examined. The robustness of all results is checked against resolution and the V-web threshold. The code for the COWS method has been made publicly available.