Reconstructing Filaments around Galaxy Clusters from Spectroscopic Surveys using Machine Learning

Abstract

Abstract Next-generation surveys such as the WEAVE Wide-Field Cluster Survey will soon give astronomers an unprecedented opportunity to study cosmic web structure and filamentary populations around clusters. Analysis of classical methods of extracting the cosmic web from simulated 2D projections has revealed significant incompleteness and contamination. In this note, we present the first results from a random forest trained and tested on the dark-matter simulation MDPL2. Our algorithm improves the precision of filament classification by 11% and decreases the structural reconstruction error by 43% compared to the previously published method.