Speaker
Description
The Large-Scale Structure (LSS) of the Universe is organised into clusters of galaxies connected by a network of filaments and underdense cosmic voids, which occupy most of the cosmic volume. This LSS can influence the formation and evolution of galaxies and affect their physical properties. However, there is currently no consensus on how to parametrise a galaxy location with respect to the LSS, and different studies can assign the same galaxy to different structures, reflecting the lack of a widely accepted and standardised criterion. In this context, we have obtained, for each galaxy of the Sloan Digital Sky Survey (SDSS), three predicted probabilities of being located in the different environments: voids, filaments, or clusters. These probabilities, derived from neural-network classifications trained on mock catalogues, provide a continuous description, quantifying the probability to belong to each structure on a scale from 0 to 100%. The aim of this work is to define a continuous unidimensional parameter that characterises the location of each spectroscopic galaxy observed by the SDSS, ranging from 0 to 1, from voids to clusters, respectively. We apply Principal Component Analysis (PCA) to reduce the three-dimensional classification into a unidimensional continuous parameter, called the PLSS parameter.
The values of the PLSS accumulate into three distinct peaks corresponding to the three main LSS environments, leaving some regions of the parameter space undersampled. To address this, we define a second complementary unidimensional parameter that uniformly samples the full range of environments. This parameter simplifies interpretation by providing a direct link between its value and the fraction of galaxies in lower and higher density regions. Both parameters are sensitive to the relative radial location of a galaxy within its host void or cluster and can distinguish between galaxies in more or less densely populated clusters.
By providing a unified and quantitative description of galaxy locations within the cosmic web through dimensionality reduction techniques, this approach facilitates the analysis of galaxy physical properties and offers a clearer and more intuitive interpretation of environmental trends. In particular, it enables a consistent framework to address fundamental questions in galaxy evolution: what are the main drivers of galaxy formation, and how does the environment regulate their evolution?