Bisous model - detecting filamentary patterns in point processes

TL;DR

The paper introduces the Bisous model, a marked point process method for detecting and quantifying filamentary structures in the cosmic web directly from galaxy distribution data, accounting for network connectivity.

Contribution

It presents a novel statistical model for filament detection that intrinsically considers network connectivity and provides tools for extracting filament spines from galaxy data.

Findings

Successfully applied to cosmological data

Generates filament probability and orientation fields

Available as open-source code on GitHub

Abstract

The cosmic web is a highly complex geometrical pattern, with galaxy clusters at the intersection of filaments and filaments at the intersection of walls. Identifying and describing the filamentary network is not a trivial task due to the overwhelming complexity of the structure, its connectivity and the intrinsic hierarchical nature. To detect and quantify galactic filaments we use the Bisous model, which is a marked point process built to model multi-dimensional patterns. The Bisous filament finder works directly with the galaxy distribution data and the model intrinsically takes into account the connectivity of the filamentary network. The Bisous model generates the visit map (the probability to find a filament at a given point) together with the filament orientation field. Using these two fields, we can extract filament spines from the data. Together with this paper we publish the computer code for the Bisous model that is made available in GitHub. The Bisous filament finder has been successfully used in several cosmological applications and further development of the model will allow to detect the filamentary network also in photometric redshift surveys, using the full redshift posterior. We also want to encourage the astro-statistical community to use the model and to connect it with all other existing methods for filamentary pattern detection and characterisation.