Download or read book Probabilistic Photometric Redshift Estimation in Massive Digital Sky Surveys Via Machine Learning written by Antonio D'Isanto and published by . This book was released on 2019* with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Probabilistic Photometric Redshifts in the Era of Petascale Astronomy written by and published by . This book was released on 2014 with total page 188 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the growth of large photometric surveys, accurately estimating photometric redshifts, preferably as a probability density function (PDF), and fully understanding the implicit systematic uncertainties in this process has become increasingly important. These surveys are expected to obtain images of billions of distinct galaxies. As a result, storing and analyzing all of these photometric redshift PDFs will be non-trivial, and this challenge becomes even more severe if a survey plans to compute and store multiple different PDFs. In this thesis, we have developed an end-to-end framework that will compute accurate and robust photometric redshift PDFs for massive data sets by using two new, state-of-the-art machine learning techniques that are based on a random forest and a random atlas, respectively. By using data from several photometric surveys, we demonstrate the applicability of these new techniques, and we demonstrate that our new approach is among the best techniques currently available. We also show how different techniques can be combined by using novel Bayesian techniques to improve the photometric redshift precision to unprecedented levels while also presenting new approaches to better identify outliers. In addition, our framework provides supplementary information regarding the data being analyzed, including unbiased estimates of the accuracy of the technique without resorting to a validation data set, identification of poor photometric redshift areas within the parameter space occupied by the spectroscopic training data, and a quantification of the relative importance of the variables used during the estimation process. Furthermore, we present a new approach to represent and store photometric redshift PDFs by using a sparse representation with outstanding compression and reconstruction capabilities. We also demonstrate how this framework can also be directly incorporated into cosmological analyses. The new techniques presented in this thesis are crucial to enable the development of precision cosmology in the era of petascale astronomical surveys.
Download or read book Machine Learning Techniques for Astrophysical Modelling and Photometric Redshift Estimation of Quasars in Optical Sky Surveys written by Neal Daniel Kumar and published by . This book was released on 2008 with total page 102 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Advances in Machine Learning and Data Mining for Astronomy written by Michael J. Way and published by CRC Press. This book was released on 2012-03-29 with total page 746 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advances in Machine Learning and Data Mining for Astronomy documents numerous successful collaborations among computer scientists, statisticians, and astronomers who illustrate the application of state-of-the-art machine learning and data mining techniques in astronomy. Due to the massive amount and complexity of data in most scientific disciplines, the material discussed in this text transcends traditional boundaries between various areas in the sciences and computer science. The book’s introductory part provides context to issues in the astronomical sciences that are also important to health, social, and physical sciences, particularly probabilistic and statistical aspects of classification and cluster analysis. The next part describes a number of astrophysics case studies that leverage a range of machine learning and data mining technologies. In the last part, developers of algorithms and practitioners of machine learning and data mining show how these tools and techniques are used in astronomical applications. With contributions from leading astronomers and computer scientists, this book is a practical guide to many of the most important developments in machine learning, data mining, and statistics. It explores how these advances can solve current and future problems in astronomy and looks at how they could lead to the creation of entirely new algorithms within the data mining community.
Download or read book Knowledge Discovery in Big Data from Astronomy and Earth Observation written by Petr Skoda and published by Elsevier. This book was released on 2020-04-10 with total page 474 pages. Available in PDF, EPUB and Kindle. Book excerpt: Knowledge Discovery in Big Data from Astronomy and Earth Observation: Astrogeoinformatics bridges the gap between astronomy and geoscience in the context of applications, techniques and key principles of big data. Machine learning and parallel computing are increasingly becoming cross-disciplinary as the phenomena of Big Data is becoming common place. This book provides insight into the common workflows and data science tools used for big data in astronomy and geoscience. After establishing similarity in data gathering, pre-processing and handling, the data science aspects are illustrated in the context of both fields. Software, hardware and algorithms of big data are addressed. Finally, the book offers insight into the emerging science which combines data and expertise from both fields in studying the effect of cosmos on the earth and its inhabitants. - Addresses both astronomy and geosciences in parallel, from a big data perspective - Includes introductory information, key principles, applications and the latest techniques - Well-supported by computing and information science-oriented chapters to introduce the necessary knowledge in these fields
Download or read book Applications of statistical methods and machine learning in the space sciences written by Bala Poduval and published by Frontiers Media SA. This book was released on 2023-04-12 with total page 203 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Astroinformatics IAU S325 written by Massimo Brescia and published by Cambridge University Press. This book was released on 2017-06-15 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Astronomy has become data-driven in ways that are both quantitatively and qualitatively different from the past: data structures are not simple; procedures to gain astrophysical insights are not obvious; and the informational content of the data sets is so high that archival research and data mining are not merely convenient, but obligatory, as researchers who obtain the data can only extract a small fraction of the science enabled by it. IAU Symposium 325 took place at a crucial stage in the development of the field, when many efforts have carried significant achievements, but the widespread groups have just begun to effectively communicate across specialties, to gather and assimilate their achievements, and to consult cross-disciplinary experts. Bringing together astronomers involved in surveys and large simulation projects, computer scientists, data scientists, and companies, this volume showcases their fruitful exchange of ideas, methods, software, and technical capabilities.
Download or read book Data Analytics and Management in Data Intensive Domains written by Leonid Kalinichenko and published by Springer. This book was released on 2018-07-12 with total page 285 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 19th International Conference on Data Analytics and Management in Data Intensive Domains, DAMDID/RCDL 2017, held in Moscow, Russia, in October 2017. The 16 revised full papers presented together with three invited papers were carefully reviewed and selected from 75 submissions. The papers are organized in the following topical sections: data analytics; next generation genomic sequencing: challenges and solutions; novel approaches to analyzing and classifying of various astronomical entities and events; ontology population in data intensive domains; heterogeneous data integration issues; data curation and data provenance support; and temporal summaries generation.
Download or read book Classification and Discovery in Large Astronomical Surveys written by Coryn Bailer-Jones and published by American Institute of Physics. This book was released on 2008-12-11 with total page 402 pages. Available in PDF, EPUB and Kindle. Book excerpt: Astronomical surveys produce large amounts of photometric, spectroscopic and time-series data. Object classification, parameter determination, novelty detection and the discovery of structure in these are challenging tasks. This book, featuring contributions from both astronomers and computer scientists, discusses a broad range of astronomical problems and shows how various machine learining and statistical analysis techniques are being used to solve them.
Download or read book Luminosity Function Using Photometric Redshifts written by Diana Hanbury and published by . This book was released on 2008 with total page 282 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book The SDSS Coadd written by and published by . This book was released on 2011 with total page 16 pages. Available in PDF, EPUB and Kindle. Book excerpt: We present and describe a catalog of galaxy photometric redshifts (photo-z's) for the Sloan Digital Sky Survey (SDSS) Coadd Data. We use the Artificial Neural Network (ANN) technique to calculate photo-z's and the Nearest Neighbor Error (NNE) method to estimate photo-z errors for H"13 million objects classified as galaxies in the coadd with r
Download or read book Star Formation Rates of Galaxies written by Andreas Zezas and published by Cambridge University Press. This book was released on 2021-04-29 with total page 318 pages. Available in PDF, EPUB and Kindle. Book excerpt: Star-formation is one of the key processes that shape the current state and evolution of galaxies. This volume provides a comprehensive presentation of the different methods used to measure the intensity of recent or on-going star-forming activity in galaxies, discussing their advantages and complications in detail. It includes a thorough overview of the theoretical underpinnings of star-formation rate indicators, including topics such as stellar evolution and stellar spectra, the stellar initial mass function, and the physical conditions in the interstellar medium. The authors bring together in one place detailed and comparative discussions of traditional and new star-formation rate indicators, star-formation rate measurements in different spatial scales, and comparisons of star-formation rate indicators probing different stellar populations, along with the corresponding theoretical background. This is a useful reference for students and researchers working in the field of extragalactic astrophysics and studying star-formation in local and higher-redshift galaxies.
Download or read book Astronomical Data Analysis Software and Systems XIV written by Patrick L. Shopbell and published by . This book was released on 2005 with total page 776 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Probabilistic Photometric Redshifts in the Era of Petascale Astronomy written by and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Frontiers in Massive Data Analysis written by National Research Council and published by National Academies Press. This book was released on 2013-09-03 with total page 191 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data mining of massive data sets is transforming the way we think about crisis response, marketing, entertainment, cybersecurity and national intelligence. Collections of documents, images, videos, and networks are being thought of not merely as bit strings to be stored, indexed, and retrieved, but as potential sources of discovery and knowledge, requiring sophisticated analysis techniques that go far beyond classical indexing and keyword counting, aiming to find relational and semantic interpretations of the phenomena underlying the data. Frontiers in Massive Data Analysis examines the frontier of analyzing massive amounts of data, whether in a static database or streaming through a system. Data at that scale-terabytes and petabytes-is increasingly common in science (e.g., particle physics, remote sensing, genomics), Internet commerce, business analytics, national security, communications, and elsewhere. The tools that work to infer knowledge from data at smaller scales do not necessarily work, or work well, at such massive scale. New tools, skills, and approaches are necessary, and this report identifies many of them, plus promising research directions to explore. Frontiers in Massive Data Analysis discusses pitfalls in trying to infer knowledge from massive data, and it characterizes seven major classes of computation that are common in the analysis of massive data. Overall, this report illustrates the cross-disciplinary knowledge-from computer science, statistics, machine learning, and application disciplines-that must be brought to bear to make useful inferences from massive data.
Download or read book NETLAB written by Ian Nabney and published by Springer Science & Business Media. This book was released on 2002 with total page 444 pages. Available in PDF, EPUB and Kindle. Book excerpt: Getting the most out of neural networks and related data modelling techniques is the purpose of this book. The text, with the accompanying Netlab toolbox, provides all the necessary tools and knowledge. Throughout, the emphasis is on methods that are relevant to the practical application of neural networks to pattern analysis problems. All parts of the toolbox interact in a coherent way, and implementations and descriptions of standard statistical techniques are provided so that they can be used as benchmarks against which more sophisticated algorithms can be evaluated. Plenty of examples and demonstration programs illustrate the theory and help the reader understand the algorithms and how to apply them.
Download or read book An Investigation of Machine learning Algorithms for the Estimation of Galaxy Redshift written by Kieran J. Luken and published by . This book was released on 2018 with total page 288 pages. Available in PDF, EPUB and Kindle. Book excerpt: The next wave of large radio telescopes is being commissioned, with plans to observe deeper, in wider areas than ever before. The Evolutionary Map of the Universe (EMU) project is expected to increase the number of known radio galaxies from ∼2.5 million to ∼70 million, allowing for statistical studies of unprecedented size in the radio regime. However, most of the studies planned by the EMU project require redshift estimates. While the redshift measurements required don't need to be measured to excellent resolution and can be roughly binned, they do require a low level of outliers. Even with recent advancements in multi-object spectroscopy, spectroscopic redshifts will only be possible for a small fraction of sources. The majority of the newly discovered radio sources will have limited multi-wavelength photometry, whereas traditional photometric template fitting methods requires high-quality, complete multiwavelength photometry. Previous research has used machine learning (ML) to estimate redshift, but has primarily focused on trying to match the best results provided by photometric template fitting, using the best, and most complete data available. For the most part, the datasets used are not radio-selected - which typically fail using photometric template fitting methods - and are limited in redshift. While Machine Learning (ML) techniques have proved to be effective, most have not been conclusively tested on radio-selected datasets, at the higher redshift ranges expected from the EMU project. In this thesis, I examine the utility of the k-NearestNeighbours (kNN) and Random Forest (RF) regression and classification algorithms for estimating the redshift of a source from its features.
