SDSS-RASS: Next Generation of Cluster-Finding Algorithms
Abstract
We outline here the next generation of cluster-finding algorithms. We show how advances in Computer Science and Statistics have helped develop robust, fast algorithms for finding clusters of galaxies in large multi-dimensional astronomical databases like the Sloan Digital Sky Survey (SDSS). Specifically, this paper presents four new advances: (1) A new semi-parametric algorithm - nicknamed ``C4'' - for jointly finding clusters of galaxies in the SDSS and ROSAT All-Sky Survey databases; (2) The introduction of the False Discovery Rate into Astronomy; (3) The role of kernel shape in optimizing cluster detection; (4) A new determination of the X-ray Cluster Luminosity Function which has bearing on the existence of a ``deficit'' of high redshift, high luminosity clusters. This research is part of our ``Computational AstroStatistics'' collaboration (see Nichol et al. 2000) and the algorithms and techniques discussed herein will form part of the ``Virtual Observatory'' analysis toolkit.
Cite
@article{arxiv.astro-ph/0011557,
title = {SDSS-RASS: Next Generation of Cluster-Finding Algorithms},
author = {R. Nichol and C. Miller and A. Connolly and S. Chong and C. Genovese and A. Moore and D. Reichart and J. Schneider and L. Wasserman and J. Annis and J. Brinkman and H. Bohringer and F. Castander and R. Kim and T. McKay and M. Postman and E. Sheldon and I. Szapudi and K. Romer and W. Voges},
journal= {arXiv preprint arXiv:astro-ph/0011557},
year = {2009}
}
Comments
To appear in Proceedings of MPA/MPE/ESO Conference "Mining the Sky", July 31 - August 4, 2000, Garching, Germany