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 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 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 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: The problem of photometric redshift estimation is a major subject in astronomy, since the need of estimating distances for a huge number of sources, as required by the data deluge of the recent years. The ability to estimate redshifts through spectroscopy does not scale with this avalanche of data. Photometric redshifts provide the required redshift estimates at the cost of some precision. The success of several forthcoming missions is highly dependent on the availability of photometric redshifts. The purpose of this thesis is to provide innovative methods for photometric redshift estimation. Two models are proposed. The first is fully-automatized, based on the combination of a convolutional neural network with a mixture density network, to predict probabilistic multimodal redshifts directly from images. The second model is features-based, performing a massive combination of photometric parameters to apply a forward selection in a huge feature space. The proposed models perform very efficiently compared to some of the most common models used in the literature. An important part of the work is dedicated to the correct estimation of the errors and prediction quality. The proposed models are very general and can be applied to different topics in astronomy and beyond.

Download or read book Photometric Redshifts and High Redshift Galaxies written by and published by . This book was released on 1999 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book The Redshift Controversy written by George B. Field and published by Addison Wesley Longman. This book was released on 1973 with total page 360 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Enhancing Photometric Redshifts for the Era of Precision Cosmology written by John Yue Han Soo and published by . This book was released on 2018 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Better Input Better Output written by John Bryce Kalmbach and published by . This book was released on 2019 with total page 163 pages. Available in PDF, EPUB and Kindle. Book excerpt: We are at the beginning of an era of large scale survey astronomy where we will soon measure photometry for billions of galaxies. In order to effectively use these galaxies for dark energy measurements we require measurements of the distances to these galaxies. Spectroscopic redshifts are not feasible for more than a small fraction of these galaxies and thus our primary distance measurements will rely on photometric redshift methods. This thesis highlights three challenges in photometric redshift estimation and techniques we developed to tackle these challenges: Using Information Theory to Optimize Bandpasses for Photometric Redshifts: We apply ideas from information theory to create a method for the design of optimal filters for photometric redshift estimation. We show the method applied to a series of simple example filters in order to motivate an intuition for how photometric redshift estimators respond to the properties of photometric passbands. We then design a realistic set of six filters covering optical wavelengths that optimize photometric redshifts for z [less than or equal to] 2. We create a simulated catalog for these optimal filters and use our filters with a photometric redshift estimation code to compare to the filters for the Large Synoptic Survey Telescope (LSST) which have key features in common with our optimal filters. Expanding Template Sets for Template Based Photo-Z Algorithms: Measuring the physical properties of galaxies such as redshift frequently requires the use of Spectral Energy Distributions (SEDs). SED template sets are, however, often small in number and cover limited portions of photometric color space. Here we present a new method to estimate SEDs as a function of color from a small training set of template SEDs. We first cover the mathematical background behind the technique before demonstrating our ability to reconstruct spectra based upon colors and then compare to other common interpolation and extrapolation methods. When the photometric filters and spectra overlap we show reduction of error in the estimated spectra of over 65% compared to the more commonly used techniques. We also show an expansion of the method to wavelengths beyond the range of the photometric filters. Finally, we demonstrate the usefulness of our technique by generating 50 additional SED templates from an original set of 10 and applying the new set to photometric redshift estimation. We are able to reduce the photometric redshifts standard deviation by at least 22.0% and the outlier rejected bias by over 86.2% compared to original set for z [less than or equal to] 3. Color Space Data Augmentation for Photometric Redshifts: When training sets for machine learning methods are not representative of the test set then there can be errors in the resulting estimates. In photometric redshifts this can happen when the color space of the spectroscopic data does not match the observed galaxy color space for an empirical photometric redshift estimation method. We first show how a lack of data in a region of color space of the training data affects photometric redshift estimation and then develop three different methods to add in synthetic training data to the missing area to mitigate the errors. Our best performing method lowers the photo-z bias by 51% and reduces the outlier fraction by 9.6% in the test data that lies in the missing area of color space compared to an unrepresentative training catalog.

Download or read book Group finding with Photometric Redshifts written by Bryan Gillis and published by . This book was released on 2010 with total page 48 pages. Available in PDF, EPUB and Kindle. Book excerpt: We present a galaxy group-finding algorithm, the Photo-z Probability Peaks (P3) algorithm, optimized for locating small galaxy groups using photometric redshift data by searching for peaks in the signal-to-noise of the local overdensity of galaxies in a 3-dimensional grid. This method is an improvement over similar matched-filter methods in reducing background contamination through the use of redshift information, allowing it to accurately detect groups to a much lower size limit. We present the results of tests of our algorithm on galaxy catalogues from the Millennium Simulation. For typical settings of our algorithm and photometric redshift accuracy of sigma_z = 0.05 it attains a purity of 84% and detects ~83 groups/deg.^2 with an average group size of 5.5 members. With photometric redshift accuracy of sigma_z = 0.02, it attains a purity of 94% and detects ~80 groups/deg.^2 with an average group size of 6.3 members. We also test our algorithm on data available for the COSMOS field and the presently-available fields from the CFHTLS-Wide survey, presenting preliminary results of this analysis.

Download or read book Redshift 2 Multimedia Astronomy written by Peter H. Raven and published by . This book was released on 1997 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Extragalactic Gas at Low Redshift written by John S. Mulchaey and published by . This book was released on 2002 with total page 396 pages. Available in PDF, EPUB and Kindle. Book excerpt:

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 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 Big Data in Complex Systems written by Aboul Ella Hassanien and published by Springer. This book was released on 2015-01-02 with total page 502 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume provides challenges and Opportunities with updated, in-depth material on the application of Big data to complex systems in order to find solutions for the challenges and problems facing big data sets applications. Much data today is not natively in structured format; for example, tweets and blogs are weakly structured pieces of text, while images and video are structured for storage and display, but not for semantic content and search. Therefore transforming such content into a structured format for later analysis is a major challenge. Data analysis, organization, retrieval, and modeling are other foundational challenges treated in this book. The material of this book will be useful for researchers and practitioners in the field of big data as well as advanced undergraduate and graduate students. Each of the 17 chapters in the book opens with a chapter abstract and key terms list. The chapters are organized along the lines of problem description, related works, and analysis of the results and comparisons are provided whenever feasible.

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 744 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

Download or read book Modern Statistical Methods for Astronomy written by Eric D. Feigelson and published by Cambridge University Press. This book was released on 2012-07-12 with total page 495 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modern Statistical Methods for Astronomy: With R Applications.

Download or read book Data Analysis in Astronomy written by V. di Gesù and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 521 pages. Available in PDF, EPUB and Kindle. Book excerpt: The international Workshop on "Data Analysis in Astronomy" was in tended to give a presentation of experiences that have been acqui red in data analysis and image processing, developments and appli cations that are steadly growing up in Astronomy. The quality and the quantity of ground and satellite observations require more so phisticated data analysis methods and better computational tools. The Workshop has reviewed the present state of the art, explored new methods and discussed a wide range of applications. The topics which have been selected have covered the main fields of interest for data analysis in Astronomy. The Workshop has been focused on the methods used and their significant applications. Results which gave a major contribution to the physical interpre tation of the data have been stressed in the presentations. Atten tion has been devoted to the description of operational system for data analysis in astronomy. The success of the meeting has been the results of the coordinated effort of several people from the organizers to those who presen ted a contribution and/or took part in the discussion. We wish to thank the members of the Workshop scientific committee Prof. M. Ca paccioli, Prof. G. De Biase, Prof. G. Sedmak, Prof. A. Zichichi and of the local organizing committee Dr. R. Buccheri and Dr. M.C. Macca rone together with Miss P. Savalli and Dr. A. Gabriele of the E. Majo rana Center for their support and the unvaluable part in arranging the Workshop.

Download or read book Big Data in Astronomy written by Linghe Kong and published by Elsevier. This book was released on 2020-06-13 with total page 440 pages. Available in PDF, EPUB and Kindle. Book excerpt: Big Data in Radio Astronomy: Scientific Data Processing for Advanced Radio Telescopes provides the latest research developments in big data methods and techniques for radio astronomy. Providing examples from such projects as the Square Kilometer Array (SKA), the world's largest radio telescope that generates over an Exabyte of data every day, the book offers solutions for coping with the challenges and opportunities presented by the exponential growth of astronomical data. Presenting state-of-the-art results and research, this book is a timely reference for both practitioners and researchers working in radio astronomy, as well as students looking for a basic understanding of big data in astronomy. - Bridges the gap between radio astronomy and computer science - Includes coverage of the observation lifecycle as well as data collection, processing and analysis - Presents state-of-the-art research and techniques in big data related to radio astronomy - Utilizes real-world examples, such as Square Kilometer Array (SKA) and Five-hundred-meter Aperture Spherical radio Telescope (FAST)