Advances in machine learning and data mining for astronomy

Front Cover......Page 1
Contents......Page 6
Foreword......Page 10
Editors......Page 12
Perspective......Page 14
Contributors......Page 26
PART I: Foundational Issues......Page 32
CHAPTER 1: Classification in Astronomy: Past and Present......Page 34
CHAPTER 2: Searching the Heavens: Astronomy, Computation, Statistics, Data Mining, and Philosophy......Page 42
CHAPTER 3: Probability and Statistics in Astronomical Machine Learning and Data Mining......Page 58
PART II: Astronomical Applications......Page 68
SECTION 1: Source Identification......Page 70
CHAPTER 4: Automated Science Processing for the Fermi Large Area Telescope......Page 72
CHAPTER 5: Cosmic Microwave Background Data Analysis......Page 86
CHAPTER 6: Data Mining and Machine Learning in Time-Domain Discovery and Classification......Page 120
CHAPTER 7: Cross-Identification of Sources: Theory and Practice......Page 144
CHAPTER 8: The Sky Pixelization for Cosmic Microwave Background Mapping......Page 164
CHAPTER 9: Future Sky Surveys: New Discovery Frontiers......Page 192
CHAPTER 10: Poisson Noise Removal in Spherical Multichannel Images: Application to Fermi Data......Page 214
SECTION 2: Classification......Page 242
CHAPTER 11: Galaxy Zoo: Morphological Classification and Citizen Science......Page 244
CHAPTER 12: The Utilization of Classifications in High-Energy Astrophysics Experiments......Page 268
CHAPTER 13: Database-Driven Analyses of Astronomical Spectra......Page 298
CHAPTER 14: Weak Gravitational Lensing......Page 318
CHAPTER 15: Photometric Redshifts: 50 Years After......Page 354
CHAPTER 16: Galaxy Clusters......Page 368
SECTION 3: Signal Processing (Time-Series) Analysis......Page 384
CHAPTER 17: Planet Detection: The Kepler Mission......Page 386
CHAPTER 18: Classification of Variable Objects in Massive Sky Monitoring Surveys......Page 414
CHAPTER 19: Gravitational Wave Astronomy......Page 438
SECTION 4: The Largest Data Sets......Page 476
CHAPTER 20: Virtual Observatory and Distributed Data Mining......Page 478
CHAPTER 21: Multitree Algorithms for Large-Scale Astrostatistics......Page 494
PART III: Machine Learning Methods......Page 516
CHAPTER 22: Time–Frequency Learning Machines for Nonstationarity Detection Using Surrogates......Page 518
CHAPTER 23: Classification......Page 536
CHAPTER 24: On the Shoulders of Gauss, Bessel, and Poisson: Links, Chunks, Spheres, and Conditional Models......Page 554
CHAPTER 25: Data Clustering......Page 574
CHAPTER 26: Ensemble Methods: A Review......Page 594
CHAPTER 27: Parallel and Distributed Data Mining for Astronomy Applications......Page 626
CHAPTER 28: Pattern Recognition in Time Series......Page 648
CHAPTER 29: Randomized Algorithms for Matrices and Data......Page 678