Ebook: Statistical Challenges in Modern Astronomy V

This volume contains a selection of chapters based on papers to be presented at the Fifth Statistical Challenges in Modern Astronomy Symposium. The symposium will be held June 13-15th at Penn State University. Modern astronomical research faces a vast range of statistical issues which have spawned a revival in methodological activity among astronomers. The Statistical Challenges in Modern Astronomy V conference will bring astronomers and statisticians together to discuss methodological issues of common interest. Time series analysis, image analysis, Bayesian methods, Poisson processes, nonlinear regression, maximum likelihood, multivariate classification, and wavelet and multiscale analyses are all important themes to be covered in detail. Many problems will be introduced at the conference in the context of large-scale astronomical projects including LIGO, AXAF, XTE, Hipparcos, and digitized sky surveys.

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Now beginning its third decade, the Statistical Challenges in Modern Astronomy (SCMA) conferences are the premier forums where astronomers and statisticians discuss advanced methodological issues arising in astronomical research. From cosmology to exoplanets, astronomers produce enormous datasets and encounter difficult modeling issues to arrive at astrophysical insights. At the SCMA V conference held at Penn State University in June 2011, researchers from around the world presented the latest astrostatistical methods. To promote cross-disciplinary perspectives, each lecture from an expert in one field is followed by a commentary from the other field.

A wide range of statistical developments are highlighted in the SCMA V conference. Some focus on problems arising in precision cosmology involving characteristics of the cosmic microwave background, galaxy clustering and gravitational lensing. Bayesian approaches are particularly important in this and other areas. Knowledge discovery from megadatasets brings methods of data mining into use. Image analysis and time series analysis are areas where astronomers perennially wrestle with sophisticated modeling problems. The proceedings ends with discussion of the future of astrostatistics.

Eric D. Feigelson, Professor of Astronomy & Astrophysics, and G. Jogesh Babu, Professor of Statistics, have long collaborated in cross-disciplinary research and services. Under the auspices of Penn State's Center for Astrostatistics, they run the SCMA conferences, offer summer schools in statistics for astronomers, produce texts and research articles promoting advances in statistical methodology in astronomy. Feigelson also conducts research in X-ray astronomy and star formation, and Babu is a mathematical statistician with interest in bootstrap methods, nonparametrics and asymptotic theory.

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Now beginning its third decade, the Statistical Challenges in Modern Astronomy (SCMA) conferences are the premier forums where astronomers and statisticians discuss advanced methodological issues arising in astronomical research. From cosmology to exoplanets, astronomers produce enormous datasets and encounter difficult modeling issues to arrive at astrophysical insights. At the SCMA V conference held at Penn State University in June 2011, researchers from around the world presented the latest astrostatistical methods. To promote cross-disciplinary perspectives, each lecture from an expert in one field is followed by a commentary from the other field.

A wide range of statistical developments are highlighted in the SCMA V conference. Some focus on problems arising in precision cosmology involving characteristics of the cosmic microwave background, galaxy clustering and gravitational lensing. Bayesian approaches are particularly important in this and other areas. Knowledge discovery from megadatasets brings methods of data mining into use. Image analysis and time series analysis are areas where astronomers perennially wrestle with sophisticated modeling problems. The proceedings ends with discussion of the future of astrostatistics.

Eric D. Feigelson, Professor of Astronomy & Astrophysics, and G. Jogesh Babu, Professor of Statistics, have long collaborated in cross-disciplinary research and services. Under the auspices of Penn State's Center for Astrostatistics, they run the SCMA conferences, offer summer schools in statistics for astronomers, produce texts and research articles promoting advances in statistical methodology in astronomy. Feigelson also conducts research in X-ray astronomy and star formation, and Babu is a mathematical statistician with interest in bootstrap methods, nonparametrics and asymptotic theory.

Content:
Front Matter....Pages i-xxiii
Front Matter....Pages 1-1
Likelihood-Free Inference in Cosmology: Potential for the Estimation of Luminosity Functions....Pages 3-19
Commentary: Likelihood-Free Inference in Cosmology: Potential for the Estimation of Luminosity Functions....Pages 21-25
Robust, Data-Driven Inference in Non-linear Cosmostatistics....Pages 27-40
Simulation-Aided Inference in Cosmology....Pages 41-57
Commentary: Simulation-Aided Inference in Cosmology....Pages 59-64
The Matter Spectral Density from Lensed Cosmic Microwave Background Observations....Pages 65-77
Commentary: ‘The Matter Spectral Density from Lensed Cosmic Microwave Background Observations’....Pages 79-82
Needlets Estimation in Cosmology and Astrophysics....Pages 83-97
Front Matter....Pages 99-99
Parameter Estimation and Model Selection in Extragalactic Astronomy....Pages 101-116
Commentary: Bayesian Model Selection and Parameter Estimation....Pages 117-125
Cosmological Bayesian Model Selection: Recent Advances and Open Challenges....Pages 127-140
Commentary: Cosmological Bayesian Model Selection....Pages 141-146
Measurement Error Models in Astronomy....Pages 147-162
Commentary: “Measurement Error Models in Astronomy” by Brandon C. Kelly....Pages 163-169
Asteroseismology: Bayesian Analysis of Solar-Like Oscillators....Pages 171-176
Semi-parametric Robust Event Detection for Massive Time-Domain Databases....Pages 177-187
Bayesian Analysis of Reverberation Mapping Data....Pages 189-195
Bayesian Mixture Models for Poisson Astronomical Images....Pages 197-202
Systematic Errors in High-Energy Astrophysics....Pages 203-207
Hierarchical Bayesian Models for Type Ia Supernova Inference....Pages 209-218
Front Matter....Pages 99-99
Bayesian Flux Reconstruction in One and Two Bands....Pages 219-224
Commentary: Bayesian Analysis Across Astronomy....Pages 225-236
Front Matter....Pages 237-237
Sparse Astronomical Data Analysis....Pages 239-253
Exploiting Non-linear Structure in Astronomical Data for Improved Statistical Inference....Pages 255-267
Surprise Detection in Multivariate Astronomical Data....Pages 269-273
On Statistical Cross-Identification in Astronomy....Pages 275-289
Commentary: On Statistical Cross-Identification in Astronomy....Pages 291-302
Data Compression Methods in Astrophysics....Pages 303-308
Commentary: Data Compression Methods in Astrophysics....Pages 309-320
Front Matter....Pages 321-325
Morphological Image Analysis and Sunspot Classification....Pages 327-327
Commentary: Morphological Image Analysis and Sunspot Classification....Pages 329-342
Learning About the Sky Through Simulations....Pages 343-346
Commentary: Learning About the Sky Through Simulations....Pages 347-359
Statistical Analyses of Data Cubes....Pages 361-366
Astronomical Transient Detection Controlling the False Discovery Rate....Pages 367-382
Commentary: Astronomical Transient Detection Controlling the False Discovery Rate....Pages 383-396
Slepian Wavelet Variances for Regularly and Irregularly Sampled Time Series....Pages 397-401
Commentary: Slepian Wavelet Variances for Regularly and Irregularly Samples Time Series....Pages 403-418
Front Matter....Pages 419-424
Astrostatistics in the International Arena....Pages 425-425
Front Matter....Pages 427-433
The R Statistical Computing Environment....Pages 425-425
Panel Discussion: The Future of Astrostatistics....Pages 435-447
Front Matter....Pages 449-465
Techniques for Massive-Data Machine Learning in Astronomy....Pages 467-467
A Bayesian Approach to Gravitational Lens Model Selection....Pages 469-472
Identification of Outliers Through Clustering and Semi-supervised Learning for All Sky Surveys....Pages 473-478
Estimation of Moments on the Sphere by Means of Fast Convolution....Pages 479-481
Variability Detection by Change-Point Analysis....Pages 483-485
Evolution as a Confounding Parameter in Scaling Relations for Galaxies....Pages 487-489
Detecting Galaxy Mergers at High Redshift....Pages 491-493
Multi-component Analysis of a Sample of Bright X-Ray Selected Active Galactic Nuclei....Pages 495-496
Applying the Background-Source Separation Algorithm to Chandra Deep Field South Data....Pages 497-498
Non-Gaussian Physics of the Cosmological Genus Statistic....Pages 499-500
Modeling Undetectable Flares....Pages 501-504
An F-Statistic Based Multi-detector Veto for Detector Artifacts in Gravitational Wave Data....Pages 505-506
Constrained Probability Distributions of Correlation Functions....Pages 507-509
Improving Weak Lensing Reconstructions in 3D Using Sparsity....Pages 511-513
Bayesian Predictions from the Semi-analytic Models of Galaxy Formation....Pages 515-517
Statistical Issues in Galaxy Cluster Cosmology....Pages 519-521
Statistical Analyses to Understand the Relationship Between the Properties of Exoplanets and Their Host Stars....Pages 523-525
Front Matter....Pages 527-529
Identifying High-z Gamma-Ray Burst Candidates Using Random Forest Classification....Pages 531-532
Theoretical Power Spectrum Estimation from Cosmic Microwave Background Data....Pages 467-467
Guilt by Association: Finding Cosmic Ray Sources Using Hierarchical Bayesian Clustering....Pages 533-534
Statistical Differences Between Swift Gamma-Ray Burst Classes Based on ?- and X-ray Observations....Pages 535-537
A Quasi-Gaussian Approximation for the Probability Distribution of Correlation Functions....Pages 539-542
New Insights into Galaxy Structure from GALPHAT....Pages 543-545
Back Matter....Pages 547-549
....Pages 551-553