Identification of Superclusters and their Properties in the Sloan Digital Sky Survey Using WHL Cluster Catalog

Abstract: Superclusters are the largest massive structures in the cosmic web on tens to hundreds of megaparsecs (Mpc) scales. They are the largest assembly of galaxy clusters in the Universe. Apart from a few detailed studies of such structures, their evolutionary mechanism is still an open question. In order to address and answer the relevant questions, a statistically significant, large catalog of superclusters covering a wide range of redshifts and sky areas is essential. Here, we present a large catalog of 662 superclusters identified using a modified $\textit{ Friends of Friends}$ algorithm applied on the WHL (Wen-Han-Liu) cluster catalog within a redshift range of $0.05 \le z \le 0.42$. We name the most massive supercluster at $z \sim 0.25$ as $\textit{Einasto Supercluster}$. We find that the median mass of superclusters is $\sim 5.8 \times 10^{15}$ M$_{\odot}$ and median size $\sim 65$ Mpc. We find that the supercluster environment slightly affects the growth of clusters. We compare the properties of the observed superclusters with the mock superclusters extracted from the Horizon Run 4 cosmological simulation. The properties of superclusters in mocks and observations are in broad agreement. We find that the density contrast of a supercluster is correlated with its maximum extent with a power law index, $\alpha \sim -2$. The phase-space distribution of mock superclusters shows that, on average, $\sim 90\%$ part of a supercluster has a gravitational influence on its constituents. We also show mock halos' average number density and peculiar velocity profiles in and around the superclusters.

• The paper identifies 662 superclusters using a modified Friends-of-Friends algorithm that adjusts for radial selection biases.
• It reports key findings with a median mass of 5.8×10^15 M☉ and a median size of 65 Mpc, including the notable Einasto Supercluster at z≈0.25.
• Comparison with Horizon Run 4 simulations validates a power-law correlation between density contrast and size, reinforcing the catalog’s reliability for cosmic structure studies.

Identification of Superclusters in the Sloan Digital Sky Survey Using the WHL Cluster Catalog

The paper "Identification of Superclusters and their Properties in the Sloan Digital Sky Survey Using WHL Cluster Catalog" by Sankhyayan et al. explores the properties and identification of superclusters within the cosmic web. By leveraging the data from the WHL cluster catalog, this study focuses on superclusters, which are the most massive structures in the Universe, composed of numerous galaxy clusters.

Objectives and Methodology

The primary objective of the study is to identify a statistically significant number of superclusters using the Sloan Digital Sky Survey (SDSS) data through the WHL cluster catalog. By employing a modified Friends of Friends (mFoF) algorithm that accounts for survey selection biases and utilising Delaunay triangulation for efficient distance calculations, the authors address the lack of comprehensive supercluster catalogs and enhance our understanding of the evolutionary mechanisms underlying these massive structures.

The mFoF algorithm presented is adjusted according to radial selection weights to link cluster data across different redshifts. This adjustment is crucial as it minimizes the percolation effects that might otherwise misrepresent the supercluster boundaries. The linking length is carefully selected to maximize the detection of potential superclusters, ensuring reliable data quality.

Key Findings

1. Catalog Development: The study successfully constructs a catalog of 662 superclusters across a redshift range of $0.05 \leq z \leq 0.42$, various newly identified structures, and some known superclusters like the Sloan Great Wall and Saraswati supercluster.
2. Supercluster Properties: The median mass of identified superclusters is approximately $5.8 \times 10^{15} M_{\odot}$ with a median size of 65 Mpc. The study also identifies the most massive supercluster, named the Einasto Supercluster, to be at redshift $z \sim 0.25$.
3. Environmental Influence: The environment of superclusters slightly influences the growth characteristics and distributions of their constituent clusters. This mass bias suggests superclusters as preferential sites of massive cluster formation.
4. Density Contrast and Correlation: A noteworthy correlation between the density contrast and size was identified, following a power-law relation, which suggests potential applications in understanding structure morphologies and growth dynamics in simulations.
5. Comparison with Simulations: Using Horizon Run 4 simulations for mock superclusters, the study reveals consistencies in size and density contrast, reinforcing the validity of using observational data combined with simulations to understand supercluster characteristics.

Implications and Future Research

The findings of this paper considerably contribute to both the theoretical and practical understanding of large-scale cosmic structures. By providing a detailed supercluster catalog, this work offers a foundation for future studies analyzing environmental effects on galaxy evolution within these massive frameworks. The identification of potential bound and unbound superclusters presents an opportunity to further investigate the large-scale structure dynamics and the role of dark matter and baryonic processes in cosmic evolution.

Future research might focus on multi-wavelength studies that explore individual superclusters to confirm the presence of gravitational influences and further investigate their potential as bound systems. Moreover, the implications of these findings for large-scale surveys, such as the upcoming DESI and Euclid missions, can enhance our ability to model and understand the universe's large-scale structure.

Overall, this work presents a significant step in cataloging cosmic superstructures and provides valuable insights into their spatial and mass distribution, laying the groundwork for future large-scale astrophysical research.