Classification and Discovery in Large Astronomical Surveys: Proceedings of the International Conference
American Inst. of Physics, Dec 11, 2008 - Computers - 394 pages
Astronomical surveys produce large amounts of photometric, spectroscopic and time-series data. Object classification, parameter determination, novelty detection and the discovery of structure in these are challenging tasks. This book, featuring contributions from both astronomers and computer scientists, discusses a broad range of astronomical problems and shows how various machine learining and statistical analysis techniques are being used to solve them.
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Photometric Classification of Stars Galaxies and Quasars in
Statistical Identification of 2XMMi Sources
38 other sections not shown
2008 American Institute algorithm analysis arXiv astrometric Astronomical Surveys astrophysical Bailer-Jones O 2008 BALQSOs baryonic Bayesian binary calibration catalog Classification and Discovery clusters color components computed correlation cosmological dark matter database dataset decision trees density detection difference image Digital Sky Survey Discovery in Large disk distribution early-type errors estimate evolution FIGURE flux fraction function Gaia Gaia mission Galactic galaxies Gaussian grid halo Hipparcos input Institute of Physics kernel Keywords Large Astronomical Surveys large number light curves LSST luminosity machine learning magnitude matrix measurements method microlensing MNRAS morphological objects observed optical PACS Pan-STARRS panel photometric photometric redshifts pixels predicted quasars Random Forest redshift sample SDSS selection shapelet shows simulations Sloan Digital Sky sources spectra spectroscopic spectrum star formation statistical stream Support Vector Machines techniques Telescope training set transient University values variable variable stars velocity wavelength X-ray