Data Mining and Machine Learning in Astronomy (2009)
Ball, Nicholas M., Brunner, Robert J.
We review the current state of data mining and machine learning in Astronomy. 'Data Mining' can have a somewhat mixed connotation from the point of view of a researcher in this field. On the one...
Myers, Adam D, White, Martin, Ball, Nicholas M.
The use of photometric redshifts in cosmology is increasing. Often, however these photo-zs are treated like spectroscopic observations, in that the peak of the photometric redshift, rather than the...
Ball, Nicholas M., Brunner, Robert J., Myers, Adam D., Strand, Natalie E., Alberts, Stacey L., Tcheng, David
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 the Fifth Data...
Robust Machine Learning Applied to Terascale Astronomical Datasets (2008)
Ball, Nicholas M., Brunner, Robert J., Myers, Adam D.
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 cluster resources...
Robust Machine Learning Applied to Terascale Astronomical Datasets (2007)
Ball, Nicholas M., Brunner, Robert J., Myers, Adam D.
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 robust classifications...
Ball, Nicholas M., Brunner, Robert J., Myers, Adam D., Strand, Natalie E., Alberts, Stacey L., Tcheng, David, ...
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 classified as quasars in...
Galaxy Colour, Morphology, and Environment in the Sloan Digital Sky Survey (2006)
Ball, Nicholas M., Loveday, Jon, Brunner, Robert J.
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 Sersic indices, and...
Ball, Nicholas M., Brunner, Robert J., Myers, Adam D., Tcheng, David
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 477,068 objects with...
Bivariate Galaxy Luminosity Functions in the Sloan Digital Sky Survey (2005)
Ball, Nicholas M, Loveday, Jon, Brunner, Robert J, Baldry, Ivan K, Brinkmann, Jon
Bivariate luminosity functions (LFs) are computed for galaxies in the New York Value-Added Galaxy Catalogue, based on the Sloan Digital Sky Survey Data Release 4. The galaxy properties investigated...
Galaxy Types in the Sloan Digital Sky Survey Using Supervised Artificial Neural Networks (2003)
Ball, Nicholas M, Loveday, Jon, Fukugita, Masataka, Nakamura, Osamu, Okamura, Sadanori, Brinkmann, Jon, ...
Supervised artificial neural networks are used to predict useful properties of galaxies in the Sloan Digital Sky Survey, in this instance morphological classifications, spectral types and redshifts....
Morphological Classification of Galaxies Using Artificial Neural Networks (2001)
The results of morphological galaxy classifications performed by humans and by automated methods are compared. In particular, a comparison is made between the eyeball classifications of 454 galaxies...