Utilizing Astroinformatics to Maximize the Science Return of the Next Generation Virgo Cluster Survey

TL;DR

This paper discusses how astroinformatics methods are applied to analyze the Next Generation Virgo Cluster Survey data, enabling efficient object detection, classification, and scientific insights into galaxy formation in a dense environment.

Contribution

It introduces astroinformatics techniques tailored for large-scale astronomical surveys, demonstrating their effectiveness on the Virgo Cluster data set.

Findings

Successful implementation of fast data mining algorithms.

Accurate photometric redshifts and cluster membership determinations.

Enhanced detection of faint and previously unseen structures.

Abstract

The Next Generation Virgo Cluster Survey is a 104 square degree survey of the Virgo Cluster, carried out using the MegaPrime camera of the Canada-France-Hawaii telescope, from semesters 2009A-2012A. The survey will provide coverage of this nearby dense environment in the universe to unprecedented depth, providing profound insights into galaxy formation and evolution, including definitive measurements of the properties of galaxies in a dense environment in the local universe, such as the luminosity function. The limiting magnitude of the survey is g_AB = 25.7 (10 sigma point source), and the 2 sigma surface brightness limit is g_AB ~ 29 mag arcsec^-2. The data volume of the survey (approximately 50 terabytes of images), while large by contemporary astronomical standards, is not intractable. This renders the survey amenable to the methods of astroinformatics. The enormous dynamic range of objects, from the giant elliptical galaxy M87 at M(B) = -21.6, to the faintest dwarf ellipticals at M(B) ~ -6, combined with photometry in 5 broad bands (u* g' r' i' z'), and unprecedented depth revealing many previously unseen structures, creates new challenges in object detection and classification. We present results from ongoing work on the survey, including photometric redshifts, Virgo cluster membership, and the implementation of fast data mining algorithms on the infrastructure of the Canadian Astronomy Data Centre, as part of the Canadian Advanced Network for Astronomical Research (CANFAR).