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Computational AstroStatistics: Fast and Efficient Tools for Analysing Huge Astronomical Data Sources by 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. is a scholarly article available to read on EtoBox.

What is Computational AstroStatistics: Fast and Efficient Tools for Analysing Huge Astronomical Data Sources about?

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.

Author

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.

Published

2001

Language

EN