Table Of ContentSpringer Series in (cid:65)(cid:115)(cid:116)(cid:114)(cid:111)(cid:115)(cid:116)(cid:97)(cid:116)(cid:105)(cid:115)(cid:116)(cid:105)(cid:99)(cid:115)
Editor-in-chief: Joseph M. Hilbe, Jet Propulsion Laboratory, and
Arizona State University, USA
Jogesh Babu, The Pennsylvania State University, USA
Bruce Bassett, University of Cape Town, South Africa
Steffen Lautitzen, Oxford University, UK
Thomas Loredo, Cornell University, USA
Oleg Malkov, Moscow State University, Russia
Jean-luc Starck, CEA/Saclay, France
David van Dyk, Imperial College, London, UK
Springer Series in Astrostatistics, #1
Forfurther volumes:
http://www.springer.com/series/1432
Joseph M. Hilbe
Editor
Astrostatistical Challenges
for the New Astronomy
Editor
Joseph M. Hilbe
Arizona State University
Tempe, AZ, USA
ISBN 978-1-4614-3507-5 ISBN 978-1-4614-3508-2 (eBook)
DOI 10.1007/978-1-4614-3508-2
Springer New York Heidelberg Dordrecht London
Library of Congress Control Number: 2012948130
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Preface
Although statistical analysis was first applied to agricultural, business, and
architectural data as well as to astronomical problems at least thirty to forty
centuries ago, astronomers have largely been estranged from statistics throughout
the past two centuries. Except for a few astronomers, the majority have paid little
attention to the advances made in statistical theory and application since the work
of Laplace and Gauss in the late 18th and early 19th centuries. Descriptive
statistical analysis, of course, has continued to be used throughout this period, but
inferential methods were largely ignored. It was not until the last decades of the
20th century that computing speed and memory allowed the development of
statistical software that was rigorous enough to entice astronomers to again
become interested in inferential statistics. During this time small groups of
astronomers and statisticians joined together developing both collaborations and
conferences to discuss statistical methodology as it applies to astronomical data.
There are a variety of reasons for this regained interest, as well as for the two-
century breach. The initial monograph in the text addresses the reasons for the
separation, and for the renewed interest.
Newly developed instruments are - and will be - gathering truly huge amounts
astronomical data which will need to be statistically evaluated and modeled. Much
of the data being generated appears to be complex, and not amenable to
straightforward traditional methods of analysis. Many astrostatisticians --
astronomers, statisticians and computation theorists having an interest in the
statistical analysis of astronomical/cosmological data -- have recognized the
inherent multifarious nature of astronomical events, and have recently turned to
Bayesian methods for analyzing astronomical data, abandoning at least in part the
traditional frequentist methods that characterized the discipline during the 19th
and 20th centuries. However, frequentist-based analyses still have an important
role in astrostatistics, as will be learned from this volume.
Except for the first two introductory chapters, the collection of monographs in
this volume present an overview of how leading astrostatisticians are currently
dealing with astronomical data. The initial article is by the editor, providing a brief
history of astrostatistics, and an overview of its current state of development. It is
an adaptation of a presentation delivered at Los Alamos National Laboratory in
October, 2011 as a seminar in the Information Science and Technology Center
v
vi Preface
seminar series. The second contribution is by Thomas Loredo of Cornell
University who presents an overview of both frequentist and Bayesian
methodologies and details how each has been misunderstood. He then outlines
considerations for future astrostatistical analyses. Prof Loredo's contribution is an
adaptation of a presentation he made on this topic at the Statistical Challenges in
Modern Astronomy V conference held at Pennsylvania State University in June,
2011. The other monographs describe ongoing state-of-the-art astrostatistical
research. Contributions to this volume are largely adaptations of selected invited
and special topics session presentations given by the authors at the 2011 ISI World
Statistics Congress in Dublin, Ireland. Several monographs have been completely
re-written from their original form for this volume, and some are entirely new.
Nearly all of the contributors as well as co-authors are recognized as leading -- if
not the leading -- astrostatisticians in the area of their research and contribution to
this book.
The value of a volume such as this rests in the fact that readers get the
opportunity to review the work of top astrostatisticians. Each selection employs a
different manner of applying statistical methods to the data being evaluated.
Articles using traditional methods utilize techniques that are common among
statisticians who model astronomical data. Together with a history and overview
of astrostatistics as a discipline, the research shared in this volume will provide
readers with a good sense of the current state of astrostatistics. The articles may
also encourage readers themselves to contribute to this new area of statistics. The
challenges are great, and may involve developing new methods of statistical
analysis. The answers that may be gained, though, address questions that are
central to astronomy and cosmology. In fact, substantial statistical work has
already been done in such astrophysical areas as high-energy astronomy (e.g., X-
ray, gamma, and cosmic ray astronomy), neutrino astrophysics, image analysis,
and both extra-solar and early galaxy formation. Even elusive areas such as
understanding dark matter and dark energy, and if a multiverse exists, are queries
that may ultimately be answered using novel applications of current statistical
functions, or they may require the application of new and innovative
astrostatistical methods yet to be developed. It will be clear on reviewing the
monographs in this text that astrostatistics is a discipline coming to its own. It is
involved with evaluating problems of cosmic concern, and as it advances it will
demand the most of both computational and statistical resources.
I wish to thank Marc Strauss, Springer statistics editor, for suggesting the
initiation of a Springer Series in Astrostatistics, and for his support for this text.
Without his assistance this book would never have been constructed. I also must
acknowledge Rajiv Monsurate for his fine work in setting up the book for
publication, and Hannah Bracken, for her editorial support. Their efforts were also
essential for this book's completion. Finally I dedicate this book to my late father,
Rader J Hilbe, who fostered my interest in both mathematics and astronomy.
Preface vii
Update:
On August 30, 2012 the Executive Board of the ISI Astrostatistics Network
approved its being re-organized as the International Astrostatistics Association
(IAA), the first professional association for the discipline of astrostatistics. The
initial officers of the IAA were also approved. Membership includes researchers
from astronomy/astrophysics, physics, statistics, the computer-information sciences
and others who are interested in the statistical analysis of astronomical data.
The IAA has three classes of membership, with no difference in benefits: regular,
Post Doc, and student. As the professional association for the discipline, the IAA
fosters the creation of astrostatistics committees and working groups within both
international and national astronomical and statistical organizations. During the
same week that the IAA was created, the International Astronomical Union
approved an IAU Astrostatistics Working Group. The IAA, IAU Astrostatistics
WG, ISI astrostatistics committee, and LSST share a common Astrostatistics and
AstroInformatics Portal web site: https://asaip.psu.edu
The IAA and ISI are co-sponsors of the Springer Series in Astrostatistics.
Contents
Preface ...................................................................................................................... v
1 Astrostatistics: A brief history and view to the future .................................. 1
Joseph M. Hilbe, Jet Propulsion Laboratory and Arizona State University
2 Bayesian astrostatistics: A backward look to the future. ............................ 15
Thomas J. Loredo, Dept of Astronomy, Cornell University
3 Understanding better (some) astronomical data using Bayesian
methods ............................................................................................................ 41
S. Andreon, INAF-Osservatorio Astronomico di Brera
4 BEAMS: separating the wheat from the chaff in supernova
analysis ............................................................................................................. 63
Martin Kunz, Institute for Theoretical Physics, Univ of Geneva, Switz;
Renée Hlozek, Dept of Astrophysical Sciences, Princeton Univ, NJ;
Bruce A. Bassett, Dept of Mathematics and Applied Mathematics,
Univ of Cape Town, SA; Mathew Smith, Dept of Physics, Univ of Western
Cape, SA; James Newling, Dept of Mathematics and Applied
Mathematics, Univ of Cape Town, SA; Melvin Varughese, Dept of
Statistical Sciences, Univ of Cape Town, SA
5 Gaussian Random Fields in Cosmostatistics. ............................................... 87
Benjamin D. Wandelt, Institut d'Astrophysique de Paris, Université
Pierre et Marie Curie, France
6 Recent advances in Bayesian inference in cosmology
and astroparticle physics thanks to the Multinest Algorithm .................. 107
Roberto Trotta, Astrophysics Group, Dept of Physics, Imperial College
London; Farhan Feroz (Cambridge), Mike Hobson (Cambridge),
and Roberto Ruiz de Austri (Univ of Valencia, Spain)
7 Extra-solar Planets via Bayesian Fusion MCMC. ..................................... 121
Philip C. Gregory, Dept of Astronomy, University of British
Columbia, Canada
ix
x Contents
8 Classification and Anomaly Detection for Astronomical
Survey Data ................................................................................................... 149
Marc Henrion, Dept of Mathematics, Imperial College, London,
UK; Daniel J. Mortlock (Imperial), David J. Hand (Imperial), and )
Axel Gandy (Imperial))
9 Independent Component Analysis for dimension reduction
classification: Hough transform and CASH Algorithm. ........................... 185
Asis Kumar Chattopadhyay, Dept of Statistics, University of Calcutta,
India; Tanuka Chattyopadhyay (Univ Calcutta), Tuli De (Univ Calcutta),
and Saptarshi Mondal (Univ Calcutta)
10 Improved cosmological constraints from a Bayesian hierarchical
model of supernova type Ia data ................................................................. 203
Marisa Cristina March, (University of Sussex); Roberto Trotta (Imperial),
Pietro Berkes (Brandeis Univ), Glenn Starkman (Case Western
Reserve Univ), Pascal Vaudrevange (DESY, Hamburg, Germany)
