# Unravelling the Cosmic Web: An analysis of the SDSS DR14 with the Local Dimension

## TL;DR

This study investigates the structure of the cosmic web using SDSS data, revealing that sheets dominate on smaller scales and transition to a more homogeneous network at larger scales, with findings supported by simulations.

## Contribution

It introduces a method to measure local galaxy environment across scales and compares observational data with simulations, highlighting the prevalence of sheets and filaments in the cosmic web.

## Key findings

- Sheets are the most common structure across scales, peaking at 30 h^{-1} Mpc.
- Filaments extend only up to 30 h^{-1} Mpc in the galaxy distribution.
- Results are consistent across different sample densities and fit criteria.

## Abstract

We analyze a volume limited galaxy sample from the SDSS to study the environments of galaxies on different length scales in the local Universe. We measure the local dimension of the SDSS galaxies on different length scales and find that the sheets or sheetlike structures are the most prevalent pattern in the cosmic web throughout the entire length scales. The abundance of sheets peaks at $30 \, h^{-1}\, {\rm Mpc}$ and they can extend upto a length scales of $90 \, h^{-1}\, {\rm Mpc}$ . Analyzing mock catalogues, we find that the sheets are non-existent beyond $30 \, h^{-1}\, {\rm Mpc}$ in the Poisson distributions. We find that the straight filaments in the SDSS galaxy distribution can extend only upto a length scale of $30 \, h^{-1}\, {\rm Mpc}$. Our results indicate that the environment of a galaxy exhibits a gradual transition towards higher local dimension with increasing length scales finally approaching a nearly homogeneous network on large scales. We compare our findings with a semi analytic galaxy catalogue from the Millennium Run simulation which are in fairly good agreement with the observations. We also test the effects of the number density of the sample and the cut-off in the goodness of fit which shows that the results are nearly independent of these factors. Finally we apply the method to a set of simulations of the segment Cox process and find that it can characterize such distributions.