Advances in Machine Learning and Data Mining for Astronomy

With contributions from leading astronomers and computer scientists, this book is a practical guide to many of the most important developments in machine learning, data mining, and statistics. It explores how these advances can solve current and future problems in astronomy and looks at how they could lead to the creation of entirely new algorithms within the data mining community.

Part I: Foundational Issues Classification in Astronomy: Past and Present, Eric Feigelson Searching the Heavens: Astronomy, Computation, Statistics, Data Mining, and Philosophy, Clark Glymour Probability and Statistics in Astronomical Machine Learning and Data Mining, Jeffrey D. Scargle
Part II: Astronomical Applications Source Identification Automated Science Processing for the Fermi Large Area Telescope, James Chiang
CMB Data Analysis, Paniez Paykari and Jean-Luc Starck Data Mining and Machine Learning in Time-Domain Discovery and Classification, Joshua S. Bloom and Joseph W. Richards Cross-Identification of Sources: Theory and Practice, Tamas Budavari The Sky Pixelization for CMB Mapping, O.V. Verkhodanov and A.G. Doroshkevich Future Sky Surveys: New Discovery Frontiers, J. Anthony Tyson and Kirk D. Borne Poisson Noise Removal in Spherical Multichannel Images: Application to Fermi Data, Jeremy Schmitt, Jean-Luc Starck, Jalal Fadili, and Seth Digel
Classification Galaxy Zoo: Morphological Classification and Citizen Science, Lucy Fortson, Karen Masters, Robert Nichol, Kirk D. Borne, Edd Edmondson, Chris Lintoot, Jordan Raddick, Kevin Schawinski, and John Wallin The Utilization of Classifications in High-Energy Astrophysics Experiments, Bill Atwood Database-Driven Analyses of Astronomical Spectra, Jan Cami Weak Gravitational Lensing, Sandrine Pires, Jean-Luc Starck, Adrienne Leonard, and Alexandre Refregier Photometric Redshifts: 50 Years after 345, Tamas Budavari Galaxy Clusters, Christopher J. Miller
Signal Processing (Time-Series) Analysis Planet Detection: The Kepler Mission, Jon M. Jenkins, Jeffrey C. Smith, Peter Tenenbaum, Joseph D. Twicken, and Jeffrey Van Cleve
Classification of Variable Objects in Massive Sky Monitoring Surveys, Przemek Wozniak, Lukasz Wyrzykowski, and Vasily Belokurov Gravitational Wave Astronomy, Lee Samuel Finn
The Largest Data Sets Virtual Observatory and Distributed Data Mining, Kirk D. Borne Multitree Algorithms for Large-Scale Astrostatistics, William B. March, Arkadas Ozakin, Dongryeol Lee, Ryan Riegel, and Alexander G. Gray
PART III: Machine Learning Methods Time--Frequency Learning Machines for Nonstationarity Detection Using Surrogates, Pierre Borgnat, Patrick Flandrin, Cedric Richard, Andre Ferrari, Hassan Amoud, and Paul Honeine Classification, Nikunj Oza On the Shoulders of Gauss, Bessel, and Poisson: Links, Chunks, Spheres, and Conditional Models, William D. Heavlin Data Clustering, Kiri L. Wagstaff Ensemble Methods: A Review, Matteo Re and Giorgio Valentini Parallel and Distributed Data Mining for Astronomy Applications, Kamalika Das and Kanishka Bhaduri
Pattern Recognition in Time Series, Jessica Lin, Sheri Williamson, Kirk D. Borne, and David De Barr Randomized Algorithms for Matrices and Data, Michael W. Mahoney
Index