LEADER 04608nam 22006855 450 001 996466709203316 005 20200704150005.0 010 $a1-280-38480-8 010 $a9786613562722 010 $a3-540-44767-9 024 7 $a10.1007/978-3-540-44767-2 035 $a(CKB)1000000000773074 035 $a(SSID)ssj0000317093 035 $a(PQKBManifestationID)11292335 035 $a(PQKBTitleCode)TC0000317093 035 $a(PQKBWorkID)10286622 035 $a(PQKB)11258305 035 $a(DE-He213)978-3-540-44767-2 035 $a(MiAaPQ)EBC3064373 035 $z(PPN)258845880 035 $a(PPN)136309488 035 $a(EXLCZ)991000000000773074 100 $a20100301d2009 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aData Analysis in Cosmology$b[electronic resource] /$fedited by Vicent J. Martinez, Enn Saar, Enrique Martinez Gonzales, Maria Jesus Pons-Borderia 205 $a1st ed. 2009. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2009. 215 $a1 online resource (XII, 636 p.) 225 1 $aLecture Notes in Physics,$x0075-8450 ;$v665 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-540-23972-3 320 $aIncludes bibliographical references and index. 327 $aUniversal Tools -- The Sea of Wavelets -- Fisher Matrices and All That: Experimental Design and Data Compression -- Data Compression, Classification and Parameter Estimation. Methods: Examples from Astronomy -- Statistics of Cosmic Background Radiation -- Cosmic Microwave Background Anisotropies: The Power Spectrum and Beyond -- Cosmic Microwave Background Polarization Analysis -- Diffuse Source Separation in CMB Observations -- Techniques for Compact Source Extraction in CMB Maps -- Determination of Cosmological Parameters from Cosmic Microwave Background Anisotropies -- Cosmic Microwave Background Data Analysis: From Time-Ordered Data to Angular Power Spectra -- Statistics of Large-Scale Structure -- The Large-Scale Structure in the Universe: From Power Laws to Acoustic Peaks -- The Cosmic Web: Geometric Analysis -- Power Spectrum Estimation. I. Basics -- Power Spectrum Estiamtion II. Linear Maximum Likelihood -- to Higher Order Spatial Statistics in Cosmology -- Phase Correlations and Topological Measures of Large-Scale Structure -- Multiscale Methods -- Gaussian Fields and Constrained Simulations of the Large-Scale Structure -- Weak Gravitational Lensing -- Mass Reconstruction from Lensing. 330 $aThe amount of cosmological data has dramatically increased in the past decades due to an unprecedented development of telescopes, detectors and satellites. Efficiently handling and analysing new data of the order of terabytes per day requires not only computer power to be processed but also the development of sophisticated algorithms and pipelines. Aiming at students and researchers the lecture notes in this volume explain in pedagogical manner the best techniques used to extract information from cosmological data, as well as reliable methods that should help us improve our view of the universe. 410 0$aLecture Notes in Physics,$x0075-8450 ;$v665 606 $aGravitation 606 $aStatistics  606 $aPhysics 606 $aClassical and Quantum Gravitation, Relativity Theory$3https://scigraph.springernature.com/ontologies/product-market-codes/P19070 606 $aStatistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences$3https://scigraph.springernature.com/ontologies/product-market-codes/S17020 606 $aNumerical and Computational Physics, Simulation$3https://scigraph.springernature.com/ontologies/product-market-codes/P19021 615 0$aGravitation. 615 0$aStatistics . 615 0$aPhysics. 615 14$aClassical and Quantum Gravitation, Relativity Theory. 615 24$aStatistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. 615 24$aNumerical and Computational Physics, Simulation. 676 $a523.10285 702 $aMartinez$b Vicent J$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aSaar$b Enn$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aGonzales$b Enrique Martinez$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aPons-Borderia$b Maria Jesus$4edt$4http://id.loc.gov/vocabulary/relators/edt 906 $aBOOK 912 $a996466709203316 996 $aData Analysis in Cosmology$9774304 997 $aUNISA