Computational AstroStatistics: Fast and Efficient Tools for Analysing Huge Astronomical Data Sources

Abstract

I present here a review of past and present multi-disciplinary research of the Pittsburgh Computational AstroStatistics² (PiCA) group. This group is dedicated to developing fast and efficient statistical algorithms for analysing huge astronomical data sources. I begin with a short review of multi-resolutional kd-trees which are the building blocks for many of our algorithms. For example, quick range queries and fast N-point correlation functions. I will present new results from the use of Mixture Models (Connolly et al. 2000) in density estimation of multi-color data from the Sloan Digital Sky Survey (SDSS). Specifically, the selection of quasars and the automated identification of X-ray sources. I will also present a brief overview of the False Discovery Rate (FDR) procedure (Miller et al. 2001a) and show how it has been used in the detection of “Baryon Wiggles” in the local galaxy power spectrum and source identification in radio data. Finally, I will look forward to new research on an automated Bayes Network anomaly detector and the possible use of the Locally Linear Embedding algorithm (LLE; Roweis & Saul 2000) for spectral classification of SDSS spectra.

This paper is followed by a commentary by statisticians Fionn D. Murtagh and Dianne Cook.

See http://www.picagroup.org for a full list of PiCA members and our latest papers, research and software