Astrostatistics and Data Mining
This volume provides an overview of the field of Astrostatistics understood as the sub-discipline dedicated to the statistical analysis of astronomical data. It presents examples of the application of the various methodologies now available to current ope
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Luis Manuel Sarro • Laurent Eyer William O’Mullane • Joris De Ridder Editors
Astrostatistics and Data Mining
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Editors Luis Manuel Sarro Department of Statistics Universidad Nacional Educacion Madrid, Spain William O’Mullane European Space Astronomy Centre Madrid, Spain
Laurent Eyer Observatoire de Gen`eve Universit´e de Gen`eve Sauverny, Switzerland Joris De Ridder Instituut voor Sterrenkunde Katholieke Universiteit Leuven Leuven, Belgium
ISBN 978-1-4614-3322-4 ISBN 978-1-4614-3323-1 (eBook) DOI 10.1007/978-1-4614-3323-1 Springer New York Heidelberg Dordrecht London Library of Congress Control Number: 2012940216 © Springer Science+Business Media New York 2012 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. Exempted from this legal reservation are brief excerpts in connection with reviews or scholarly analysis or material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work. Duplication of this publication or parts thereof is permitted only under the provisions of the Copyright Law of the Publisher’s location, in its current version, and permission for use must always be obtained from Springer. Permissions for use may be obtained through RightsLink at the Copyright Clearance Center. Violations are liable to prosecution under the respective Copyright Law. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. While the advice and information in this book are believed to be true and accurate at the date of publication, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)
Foreword
These are very exciting times for Astronomy. The availability of scientific archives from large-scale surveys is changing the way in which science is done by allowing astronomers to pose new questions. These questions cannot be answered following the traditional approaches, but need be investigated using new techniques mainly derived from the field of Statistics and Machine Learning. Furthermore, these techniques have to be adapted for parallel processing in large computational infrast
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