Springer Series in Astrostatistics

Transcription

1 Springer Series in Astrostatistics For further volumes:

2

3 Luis Manuel Sarro Laurent Eyer William O Mullane Joris De Ridder Editors Astrostatistics and Data Mining 123

4 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 Genève Université degenève Sauverny, Switzerland Joris De Ridder Instituut voor Sterrenkunde Katholieke Universiteit Leuven Leuven, Belgium ISBN ISBN (ebook) DOI / Springer New York Heidelberg Dordrecht London Library of Congress Control Number: 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 (

5 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 infrastructures. If astronomers want to survive the so-called data tsunami, all these aspects (methodological, algorithmic, and computational) will have to be tackled simultaneously, for none of them is independent of the others. The workshop that took place in La Palma in 2011 was an attempt to bring together astronomers and computer scientists from many domains in order to prepare for the data tsunami that awaits us: Gaia, Pan-STARRS and LSST are only the avant-garde of a new way of doing Science. The conference together with the Summer School was an initiative of the Spanish Virtual Observatory supported by the European Science Foundation through the GREAT programme, the Spanish Ministry of Science, and the Universidad Nacional de Educación a Distancia. v

6

7 Organising Committee Luis M. Sarro (Chairman),Universidad Nacional de Educación a Distancia, Juan del Rosal 16, Madrid, Spain Coryn Bailer-Jones, Max-Planck-Institut für Astronomie, Königstuhl 17, Heidelberg, Germany Laurent Eyer, Observatoire de Genève, Université degenève, 51 Chemin des Maillettes,1290 Sauverny, Switzerland William O Mullane, European Space Astronomy Center of ESA, Madrid, Spain Joris De Ridder, Instituut voor Sterrenkunde, K. U. Leuven, Celestijnenlaan 200D, 3001 Leuven, Belgium vii

8

9 Contents Part I Invited Talks Recent Advances in Cosmological Bayesian Model Comparison... 3 Roberto Trotta Science from Gaia: How to Deal with a Complex Billion-Source Catalogue and Data Archive Anthony G.A. Brown Extracting Knowledge from Massive Astronomical Data Sets Massimo Brescia, Stefano Cavuoti, George S. Djorgovski, Ciro Donalek, Giuseppe Longo, and Maurizio Paolillo The Art of Data Science Matthew J. Graham Part II Contributed Talks The Distribution of Galaxies in Spectral Space Y. Ascasibar and J. Sánchez Almeida Utilizing Astroinformatics to Maximize the Science Return of the Next Generation Virgo Cluster Survey Nicholas M. Ball Adjustment of Observational Data to Specific Functional Forms Using a Particle Swarm Algorithm and Differential Evolution: Rotational Curves of a Spiral Galaxy as Case Study Miguel Cárdenas-Montes, Mercedes Mollá, Miguel A. Vega-Rodríguez, Juan José Rodríguez-Vázquez, and Antonio Gómez-Iglesias Probabilistic Description of Stellar Ensembles Miguel Cerviño ix

10 x Contents Solar System Parameters from the Gaia Small Solar System Object Data Pedro David, Jerome Berthier, and Daniel Hestroffer Data Management at Gaia Data Processing Centers Pilar de Teodoro, Alexander Hutton, Benoit Frezouls, Alain Montmory, Jordi Portell, Rosario Messineo, Marco Riello, and Krzysztof Nienartowicz Hipparcos Variable Star Detection and Classification Efficiency P. Dubath, I. Lecoeur-Taïbi, L. Rimoldini, M. Süveges,J.Blomme,M.L opez, L.M. Sarro, J. De Ridder, J. Cuypers, L. Guy, K. Nienartowicz, A. Jan, M. Beck, N. Mowlavi, P. De Cat, T. Lebzelter, and L. Eyer Distributed Genetic Algorithm for Feature Selection in Gaia RVS Spectra: Application to ANN Parameterization Diego Fustes, Diego Ordóñez, Carlos Dafonte, Minia Manteiga, and Bernardino Arcay Efficient Calculation of Covariances for Astrometric Data in the Gaia Catalogue Berry Holl, Lennart Lindegren, and David Hobbs Bayesian Analysis of Cosmic Structures Francisco-Shu Kitaura Generalized Stellar Parametrizer with Gaia Photometry Data Chao Liu and Coryn A.L. Bailer-Jones Classification of Poorly Time Sampled Light Curves of Periodic Variable Stars James P. Long, Joshua S. Bloom, Noureddine El Karoui, John Rice, and Joseph W. Richards Handling Imbalanced Data Sets in Multistage Classification M. López A New Approach to the Optimization of the Extraction of Astrometric and Photometric Information from Multi-wavelength Images in Cosmological Fields Maria Jose Marquez Statistical Analysis of Caustic Crossings in Multiply Imaged Quasars T. Mediavilla, O. Ariza, E. Mediavilla, P. Alvarez Stellar Age and Mass Determination N. Mowlavi and L. Eyer Data Mining of the MultiDark Simulation Adrian M. Partl and the MultiDark Database Collaboration

11 Contents xi Overcoming Sample Selection Bias in Variable Star Classification Joseph W. Richards Data Mining on Ice Tim Ruhe, Katharina Morik, and Benjamin Schowe The Catalogue of X-Ray Bursts Detected by JEM-X Onboard INTEGRAL Celia Sánchez-Fernández The Discrete Source Classifier in Gaia-Apsis K.W. Smith A Checklist for Planning Research Data Management Gabriel Stöckle and the WissGrid team Efficient Use of Simultaneous Multi-Band Observations forvariablestaranalysis Maria Süveges, Paul Bartholdi, Andrew Becker, Željko Ivezić, Mathias Beck, and Laurent Eyer Parametrization of Binary Stars with Gaia Observations P. Tsalmantza and C.A.L. Bailer-Jones

12