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

• Main Authors: Nichol, R. C, Chong, S, Connolly, A. J, Davies, S, Genovese, C, Hopkins, A. M, Miller, C. J, Moore, A. W, Pelleg, D, Richards, G. T, Schneider, J, Szapudi, I, Wasserman, L
• Format: Journal Article
• Language: English
• Published: 09.10.2001
• Subjects: Physics - Astrophysics of Galaxies; Physics - Cosmology and Nongalactic Astrophysics; Physics - Earth and Planetary Astrophysics; Physics - High Energy Astrophysical Phenomena; Physics - Instrumentation and Methods for Astrophysics; Physics - Solar and Stellar Astrophysics
• DOI: 10.48550/arxiv.astro-ph/0110230

I present here a review of past and present multi-disciplinary research of the Pittsburgh Computational AstroStatistics (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.