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Robust Machine Learning Applied to Terascale Astronomical Datasets
Nicholas M. Ball, Robert J. Brunner, Adam D. Myers
We present recent results from the LCDM (Laboratory for Cosmological Data Mining; http://lcdm.astro.uiuc.edu) collaboration between UIUC Astronomy and NCSA to deploy supercomputing…
Robust Machine Learning Applied to Astronomical Datasets III: Probabilistic Photometric Redshifts for Galaxies and Quasars in the SDSS and GALEX
Nicholas M. Ball, Robert J. Brunner, Adam D. Myers +3
We apply machine learning in the form of a nearest neighbor instance-based algorithm (NN) to generate full photometric redshift probability density functions (PDFs) for objects in…
Robust Machine Learning Applied to Terascale Astronomical Datasets
Nicholas M. Ball, Robert J. Brunner, Adam D. Myers
We present recent results from the Laboratory for Cosmological Data Mining (http://lcdm.astro.uiuc.edu) at the National Center for Supercomputing Applications (NCSA) to provide rob…
Robust Machine Learning Applied to Astronomical Datasets II: Quantifying Photometric Redshifts for Quasars Using Instance-Based Learning
Nicholas M. Ball, Robert J. Brunner, Adam D. Myers +4
We apply instance-based machine learning in the form of a k-nearest neighbor algorithm to the task of estimating photometric redshifts for 55,746 objects spectroscopically classifi…
Galaxy Colour, Morphology, and Environment in the Sloan Digital Sky Survey
Nicholas M. Ball, Jon Loveday, Robert J. Brunner
We use the Fourth Data Release of the Sloan Digital Sky Survey to investigate the relation between galaxy rest frame u-r colour, morphology, as described by the concentration and S…
Robust Machine Learning Applied to Astronomical Datasets I: Star-Galaxy Classification of the SDSS DR3 Using Decision Trees
Nicholas M. Ball, Robert J. Brunner, Adam D. Myers +1
We provide classifications for all 143 million non-repeat photometric objects in the Third Data Release of the Sloan Digital Sky Survey (SDSS) using decision trees trained on 477,0…