Spatial Clustering of Galaxies in Large Datasets

TL;DR

This paper presents a scalable framework for analyzing spatial clustering of galaxies in large datasets, enabling efficient processing of massive astronomical catalogs like SDSS and future surveys.

Contribution

The authors develop an integrated, efficient system combining databases, masking tools, and fast correlation algorithms for large-scale galaxy clustering analysis.

Findings

Enabled analysis of SDSS galaxy clustering with unprecedented efficiency

Demonstrated the framework's scalability for future large datasets

Provided methods to estimate photometric error effects

Abstract

Datasets with tens of millions of galaxies present new challenges for the analysis of spatial clustering. We have built a framework that integrates a database of object catalogs, tools for creating masks of bad regions, and a fast (NlogN) correlation code. This system has enabled unprecedented efficiency in carrying out the analysis of galaxy clustering in the SDSS catalog. A similar approach is used to compute the three-dimensional spatial clustering of galaxies on very large scales. We describe our strategy to estimate the effect of photometric errors using a database. We discuss our efforts as an early example of data-intensive science. While it would have been possible to get these results without the framework we describe, it will be infeasible to perform these computations on the future huge datasets without using this framework.