Research Highlights

Illinois (2004-2009)

Robust Machine Classification of Large Astronomical Surveys III: Probabilistic Photometric Redshifts for Galaxies and Quasars in the SDSS and GALEX

Ball et al. 2008, ApJ 683 12

Provided full probability density functions in redshift for SDSS and SDSS+GALEX galaxies and quasars

Generation of the PDFs takes into account the errors on the observations

Can eliminate quasar catastrophic failures using single-peaked PDFs

Cause of bad quasar photo-zs are likely reddening, degeneracy, emission lines simulating other lines, and lines crossing filter edges

The method uses a large number of distance calculations, for which conventional or novel supercomputing hardware may be utilized to enable a direct extension to petascale datasets

Robust Machine Classification of Large Astronomical Surveys II: Photometric Redshifts for Quasars in the SDSS DR5

Ball et al. 2007, ApJ 663 774

Improved quasar photometric redshifts using instance-based learning

Demonstrated dramatically improved redshifts for objects cross-matched to GALEX

The results are realistic for application to further surveys because they are blind-tested

Robust Machine Classification of Large Astronomical Surveys I: Star-Galaxy Separation in the SDSS DR3

Ball et al. 2006, ApJ 650 497

First application of machine learning to an astronomical dataset of ~10⁸ objects, the SDSS

Improved the SDSS star-galaxy separation to a probabilistic measure that enables selection of cosmological datasets according to the desired completeness and efficiency

Assigned each object within a framework of star, galaxy, or neither-star-nor-galaxy to highlight astrophysically interesting objects, e.g., quasars

Created a state-of-the-art sample of quasars

Demonstrated the efficacy of the method when blind-tested on the 2dFGRS and the 2QZ

Sussex (2001-2004)

Galaxy Morphology, Colour and Environment in the Sloan Digital Sky Survey

Ball, Loveday & Brunner 2008, MNRAS 383 907

First study of the of the morphology-density relation using detailed morphology and comparison to color-density

When split by density and luminosity the color, Sersic profile, and CI are well described by a sum of 2 Gaussians, but the Hubble type is much less clear

When morphology is removed there is a residual color density relation, but not vice-versa

This implies that either the morphology is simply a byproduct of color, or that a single 'type' and density constitutes insufficient information

Bivariate Galaxy Luminosity Functions in the Sloan Digital Sky Survey

Ball et al. 2006, MNRAS 373 845

First study of the SDSS bivariate LF with an extensive set of galaxy properties including morphological type

A wealth of new detail seen, including clear variation in the LFs according to absolute magnitude and the second parameter

Consistent with the now standard bimodal population: early type, bright, concentrated, red vs. late/dim/not C/blue/star forming

Bimodal not well fit by a single underlying function (Schechter-Gaussian, aka. Choloniewski function)

Galaxy Types in the Sloan Digital Sky Survey Using Supervised Artificial Neural Networks

Ball et al. 2004, MNRAS 348 1038

First study to provide detailed morphological classifications of a large sample of galaxies in a digital sky survey

ANNs were used to provide Hubble types for 26,536 galaxies, to the same accuracy as human experts

Provided spectral types and photometric redshifts within the same framework

Used a more advanced training algorithm, Levenberg-Marquardt, than previously used in astronomy