Data Analysis In Cosmology

Author: Vicent J. Martinez
Editor: Springer Science & Business Media
ISBN: 3540239723
File Size: 19,34 MB
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The amount of cosmological data has dramatically increased in the past decades due to an unprecedented development of telescopes, detectors and satellites. Efficiently handling and analysing new data of the order of terabytes per day requires not only computer power to be processed but also the development of sophisticated algorithms and pipelines. Aiming at students and researchers the lecture notes in this volume explain in pedagogical manner the best techniques used to extract information from cosmological data, as well as reliable methods that should help us improve our view of the universe.

Data Analysis In Astronomy Iv

Author: International Workshop on Data Analysis in Astronomy
Editor: Plenum Publishing Corporation
ISBN:
File Size: 61,35 MB
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Reports the main results of an international workshop in Erice, Sicily, April 1991, at which astronomers and computer scientists reviewed the evolution of data analysis in astronomy during the past decade. The 32 papers cover systems and archives; the HST, ROSAT, and GRO missions, and future mission

Data Analysis Of Cosmic Microwave Background Experiments

Author: Matthew Edmund Abroe
Editor:
ISBN:
File Size: 19,67 MB
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Statistical Analysis Of Cosmic Microwave Background Anisotropy

Author: Emory Freeman Bunn
Editor:
ISBN:
File Size: 30,78 MB
Format: PDF
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Non Linear Data Analysis On The Sphere

Author: Gregor Rossmanith
Editor: Springer Science & Business Media
ISBN: 3319003097
File Size: 14,42 MB
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This work deals with the search for signatures of non-Gaussianities in the cosmic microwave background (CMB). Probing Gaussianity in the CMB addresses one of the key questions in modern cosmology because it allows us to discriminate between different models of inflation, and thus concerns a fundamental part of the standard cosmological model. The basic goal here is to adapt complementary methods stemming from the field of complexity science to CMB data analysis. Two key concepts, namely the method of surrogates and estimators for local scaling properties, are applied to CMB data analysis. All results show strong non-Gaussianities and pronounced asymmetries. The consistency of the full sky and cut sky results shows convincingly for the first time that the influence of the Galactic plane is not responsible for these deviations from Gaussianity and isotropy. The findings seriously call into question predictions of isotropic cosmologies based on the widely accepted single field slow roll inflation model.

Data Analysis In Astronomy Iv

Author: International Workshop on Data Analysis in Astronomy
Editor: Plenum Publishing Corporation
ISBN: 9780306441066
File Size: 36,31 MB
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Reports the main results of an international workshop in Erice, Sicily, April 1991, at which astronomers and computer scientists reviewed the evolution of data analysis in astronomy during the past decade. The 32 papers cover systems and archives; the HST, ROSAT, and GRO missions, and future mission

Automated Data Analysis In Astronomy

Author: Ranjan Gupta
Editor: Alpha Science International, Limited
ISBN:
File Size: 68,74 MB
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This work covers data analysis techniques in astronomy, especially the fast and automated means of data analysis. Topics include information on astronomical catalogues, databases and large surveys, and the basics of artificial neural networks and principal component analysis. Also covered is an introduction to the latest data analysis techniques being followed at several astronomical observatories in the areas of imaging, spectroscopy and photometry.

Statistics Data Mining And Machine Learning In Astronomy

Author: Željko Ivezić
Editor: Princeton University Press
ISBN: 0691151687
File Size: 23,96 MB
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As telescopes, detectors, and computers grow ever more powerful, the volume of data at the disposal of astronomers and astrophysicists will enter the petabyte domain, providing accurate measurements for billions of celestial objects. This book provides a comprehensive and accessible introduction to the cutting-edge statistical methods needed to efficiently analyze complex data sets from astronomical surveys such as the Panoramic Survey Telescope and Rapid Response System, the Dark Energy Survey, and the upcoming Large Synoptic Survey Telescope. It serves as a practical handbook for graduate students and advanced undergraduates in physics and astronomy, and as an indispensable reference for researchers. Statistics, Data Mining, and Machine Learning in Astronomy presents a wealth of practical analysis problems, evaluates techniques for solving them, and explains how to use various approaches for different types and sizes of data sets. For all applications described in the book, Python code and example data sets are provided. The supporting data sets have been carefully selected from contemporary astronomical surveys (for example, the Sloan Digital Sky Survey) and are easy to download and use. The accompanying Python code is publicly available, well documented, and follows uniform coding standards. Together, the data sets and code enable readers to reproduce all the figures and examples, evaluate the methods, and adapt them to their own fields of interest. Describes the most useful statistical and data-mining methods for extracting knowledge from huge and complex astronomical data sets Features real-world data sets from contemporary astronomical surveys Uses a freely available Python codebase throughout Ideal for students and working astronomers

Data Analysis In Astronomy Iii

Author: V. di Gesù
Editor: Springer
ISBN:
File Size: 60,16 MB
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In the book are reported the main results presented at the Third International Workshop on Data Analysis in Astronomy, held at the EUore Majorana Center for Scientific Culture, Erice, Sicily, Italy, on June 20-27,1988. The Workshop was the natural evolution of the two previous ones. The main goal of the first edition (Erice 1984) was to start a scientific interaction between Astronomers and Computer Scientists. Aim of the second (Erice 1986) was to look at the progress in data analysis methods and dedicated hardware technology. Data analysis problems become harder whenever the data are poor in statistics or the signal is weak and embedded in structured background. Experiments collecting data of such a nature require new and non-standard methodologies. Possibilistic approaches could be merged with the statistical ones, in order to formalize all the knowledge used by the scientists to reach conclusions. Moreover, the last decade has been characterized by very fast developments of Intelligent Systems for data analysis (knowledge based systems, ... ) that would be useful to support astronomers in complex decision making. For these reasons, the last edition of the workshop was intended to provide an overview on the state of the art in the data analysis methodologies and tools in the new frontieres of the astrophysics (y-astronomy, neutrino astronomy, gravitational waves, background radiation and extreme cosmic ray energy spectrum). The book is organized in two sections: - Data analysis methods and tools, - New frontieres in astronomy.

Research And Technology Objectives And Plans Summary Rtops

Author:
Editor:
ISBN:
File Size: 18,80 MB
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Scientific And Technical Aerospace Reports

Author:
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ISBN:
File Size: 59,98 MB
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Modern Cosmology

Author: Scott Dodelson
Editor: Academic Press
ISBN: 9780122191411
File Size: 65,88 MB
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An advanced text for senior undergraduates, graduate students and physical scientists in fields outside cosmology. This is a self-contained book focusing on the linear theory of the evolution of density perturbations in the universe, and the anisotropiesin the cosmic microwave background.

3rd Eso St Ecf Data Analysis Workshop

Author: Preben J. Grosbøl
Editor:
ISBN:
File Size: 22,20 MB
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Statistical Challenges In Modern Astronomy

Author: Eric D. Feigelson
Editor: Springer Science & Business Media
ISBN: 146139290X
File Size: 64,48 MB
Format: PDF
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Modern astronomy has been characterized by an enormous growth in data acquisition - from new technologies in telescopes, detectors, and computation. One can now compile catalogs of tens or hundreds of millions of stars or galaxies and databases from satellite-based observations are reaching terabit proportions. This wealth of data gives rise to statistical challenges not previously encountered in astronomy. This book is the result of a workshop held at Pennsylvania State University in August 1991 that brought together leading astronomers and statisticians to consider statistical challenges encountered in modern astronomical research. The chapters have all been thoroughly revised in the light of the discussions at the conference, and some of the lively discussion is recorded here as well.

Report On The Activities Of Space Science Department 1997 1998

Author: Agence spatiale européenne
Editor:
ISBN:
File Size: 38,27 MB
Format: PDF
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Comparative Cosmology

Author: Doug Hellinger
Editor:
ISBN:
File Size: 44,65 MB
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Statistical Challenges In Astronomy

Author: Eric D. Feigelson
Editor: Springer Science & Business Media
ISBN: 0387215298
File Size: 36,54 MB
Format: PDF, Kindle
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Digital sky surveys, high-precision astrometry from satellite data, deep-space data from orbiting telescopes, and the like have all increased the quantity and quality of astronomical data by orders of magnitude per year for several years. Making sense of this wealth of data requires sophisticated statistical techniques. Fortunately, statistical methodologies have similarly made great strides in recent years. Powerful synergies thus emerge when astronomers and statisticians join in examining astrostatistical problems and approaches. The book begins with an historical overview and tutorial articles on basic cosmology for statisticians and the principles of Bayesian analysis for astronomers. As in earlier volumes in this series, research contributions discussing topics in one field are joined with commentary from scholars in the other. Thus, for example, an overview of Bayesian methods for Poissonian data is joined by discussions of planning astronomical observations with optimal efficiency and nested models to deal with instrumental effects. The principal theme for the volume is the statistical methods needed to model fundamental characteristics of the early universe on its largest scales.