Publications

The NASA ADS links contain the final published papers, and may require a journal subscription. The arXiv astro-ph links contain preprints, but are freely available.

• Papers on ADS

• Papers on ArXiv

• Papers on Google Scholar

Invited Review

• • Ball N.M. & Brunner R.J., 2010, Data Mining and Machine Learning in Astronomy. International Journal of Modern Physics D 19 (7), pp 1049-1106, arXiv/0906.2173, NED

In Preparation

• • Ball N.M., et al., 2012, Photometric Redshifts for the MegaPipe Reductions of the Canada-France-Hawaii Telescope Legacy Survey (ApJ/AJ/MNRAS)

  • Borne K., Ball N.M., D'Abrusco R., Longo G., Mahabal A., McConnell S., 2012, Data Mining: Astronomical Discoveries through Exploration of Big Data (Invited review for New Astronomy Reviews special issue on Next Generation Sky Surveys)

  • Ferrarese L.F., et al., 2012, Missing Satellites and the Luminosity Function in the Core of the Virgo Cluster (ApJ)

  • Woods D., Mei S., Huertas M., Ilbert O., Ball N.M., et al., 2012, Photometric Redshift Estimation for the NGVS (ApJ)

  • Ball N.M., Ferrarese L.F., et al., 2013, The Luminosity and Mass Functions of Baryonic Structures in the Virgo Cluster (ApJ)

Refereed

• • Ball N.M., 2012, Utilizing Astroinformatics to Maximize the Science Return of the Next Generation Virgo Cluster Survey. Astrostatistics and Data Mining in Large Astronomical Databases, Springer, arXiv/1110.5685, hi-res PDF

  • Ferrarese L.F., et al., 2012, The Next Generation Virgo Cluster Survey (NGVS). I. Introduction to the Survey, ApJS 200 4

  • Gaudet S., Hill N., Armstrong P., Ball N.M., et al., 2010, CANFAR: the Canadian Advanced Network for Astronomical Research. In: Proc. SPIE, Software and Cyberinfrastructure in Astronomy, eds. Radziwill N.M. and Bridger A., 7740-1L, ADS

  • Myers A.D, White M. & Ball N.M., 2009, Incorporating Photometric Redshift Probability Density Information Into Real-Space Clustering Measurements, MNRAS 399 2279, arXiv/0903.3121

  • Ball N.M., Brunner R.J., Myers A.D., Strand N.E., Alberts S.L., Tcheng D., 2008, Robust Machine Learning Applied to Astronomical Datasets III: Probabilistic Photometric Redshifts for Galaxies and Quasars in the SDSS and GALEX, ApJ 683 12, arXiv/0804.3413

  • Ball N.M., Loveday J., Brunner R.J., 2008, Galaxy Colour, Morphology, and Environment in the Sloan Digital Sky Survey, MNRAS 383 907, astro-ph/0610171

  • Ball N.M., Brunner R.J., Myers A.D., Strand N.E., Alberts S.L., Tcheng D., Llora X., 2007, Robust Machine Learning Applied to Astronomical Datasets II: Quantifying Photometric Redshifts for Quasars Using Instance-Based Learning, ApJ 663 774, astro-ph/0612471

  • Ball N.M., Brunner R.J., Myers A.D., Tcheng D., 2006, Robust Machine Learning Applied to Astronomical Datasets I: Star-Galaxy Classification of the SDSS DR3 Using Decision Trees, ApJ 650 497, astro-ph/0606541

  • Ball N.M., Loveday J., Brunner R.J., Baldry I.K., Brinkmann, J., 2006, Bivariate Galaxy Luminosity Functions in the Sloan Digital Sky Survey, MNRAS 373 845, astro-ph/0507547

  • Ball N.M., Loveday J., Fukugita M., Nakamura O., Okamura S., Brinkmann J., Brunner R.J., 2004, Galaxy Types in the Sloan Digital Sky Survey Using Supervised Artificial Neural Networks, MNRAS 348 1038, astro-ph/0306390

Conference Proceedings

• • Ball N.M., 2011, Discussion on "Techniques for Massive-Data Machine Learning in Astronomy" by A. Gray. Invited commentary, Statistical Challenges in Modern Astronomy V, Springer, arXiv/1110.5688

  • Gaudet S., the CADC team, 2011, Virtualization and Grid Utilization within the CANFAR Project. Astronomical Data Analysis Software & Systems (ADASS) XX. ASP Conference Proceedings, Vol. 442, eds. Evans I.N., Accomazzi A., Mink D.J., Rots A.H., pp 61–64, ASP, San Francisco, ADS

  • Mei S., et al., 2011, The Next Generation Virgo Cluster Survey: Status and First Results. Societe Francaise d'Astronomie et d'Astrophysique (SF2A), eds. Alecian G., Belkacem K., Collin S., Samadi R., Valls-Gabaud D.

  • Ball N.M., Brunner R.J., Myers A.D., 2009, Robust Machine Learning Applied to Terascale Astronomical Datasets. In: The 9th LCI International Conference on High-Performance Clustered Computing, arXiv/0804.3417

  • Ball N.M., Brunner R.J., Myers A.D., 2008, Robust Machine Learning Applied to Terascale Astronomical Datasets. In: Astronomical Data Analysis Software & Systems (ADASS) XVII, ASP Confererence Proceedings, Vol. 394, eds. Argyle R.W., Bunclark P.S., Lewis J.R, pp 201-204, arXiv/0710.4482

  • Ball N.M., Loveday J., 2004, Galaxy Types and Luminosity Functions in the Sloan Digital Sky Survey Using Artificial Neural Networks. In: Penetrating Bars Through Masks of Cosmic Dust: The Hubble Tuning Fork Strikes a New Note, Springer, pp 771-772, ADS

  • Moore J.R., Watts M.D., Ball N.M., Ratcliff M.D., 2000, The Geology of the Ford Creek Area, Northern Montana Thrust Belt. In: Montana/Alberta thrust belt and adjacent foreland. 50th Anniversary Symposium of the Montana Geological Society, Volume 1, pp 151-168, Montana Geological Society, Billings, Montana, USA

Posters

• • Ball N.M., 2013, CANFAR + Skytree: Mining Massive Datasets as an Essential Part of the Future of Astronomy, 221st Meeting of the American Astronomical Society, Long Beach, CA, PDF

  • Ball N.M., 2012, CANFAR + Skytree: A Cloud Computing and Data Mining System for Astronomy, Astronomical Data Analysis Software and Systems XXII, Champaign, IL, PDF

  • Ball N.M., Gray A., Hack, M., Schade D., 2012, Mining Massive Datasets with CANFAR and Skytree, Astroinformatics 2012, Redmond, WA, PDF

  • Ball N.M., Schade, D., 2012, Machine Learning on the Cloud: Combining CANFAR and Skytree to Build the World's First Science Analytics System, From Data to Knowledge: Machine-Learning with Real-time and Streaming Applications, Berkeley, CA, PDF

  • Ball N.M., 2012, Astroinformatics, Cloud Computing, and New Science at the Canadian Astronomy Data Centre, 219th Meeting of the American Astronomical Society, Austin, TX, PDF

  • Ball N.M., 2011, Astroinformatics at the Canadian Astronomy Data Centre, Astroinformatics 2011, Sorrento, Italy

  • Fabbro, S., the CADC team, 2011, The Canadian Advanced Network For Astronomical Research, 217th Meeting of the American Astronomical Society, Seattle, WA, ADS

  • Ball N.M., Schade D, the CADC team, 2011, The Canadian Astronomy Data Centre, 217th Meeting of the American Astronomical Society, Seattle, WA, PDF

  • Ball N.M., Myers A.D., White M., Hickox R.C., Brunner R.J., 2010, Breaking The Quasar L-z Degeneracy Using PDF-weighted Quasar-galaxy Cross-correlations In Deep, Wide NASA Fields, 215th Meeting of the American Astronomical Society, Washington, DC, PDF

  • Ball N.M., Brunner R.J. & Myers, A.D., 2007, Robust Machine Learning Applied to Terascale Astronomical Datasets, Cosmic Cartography, Chicago, IL, PDF

  • Ball N.M., Brunner R.J., Myers A.D., Tcheng D., 2006, Robust Classification of 143 Million SDSS Objects Via Decision Tree Learning Algorithms, 208th Meeting of the American Astronomical Society, Calgary, AB, PDF

Other

• • Ball N.M. & Schade D., 2010, Astroinformatics in Canada, white paper for the Canadian Long Range Plan in astronomy (LRP2010), PDF

  • Ball N.M., 2004, Galaxy Types, Luminosity Functions and Environment in the Sloan Digital Sky Survey, PhD Thesis

  • Ball N.M., 2001, Morphological Classification of Galaxies Using Artificial Neural Networks, Masters Thesis, astro-ph/0110492

  • Ball N.M., 2001, Book Review - Cosmology Revealed: Living Inside the Cosmic Egg (Anthony Fairall), The Observatory, 121, 345