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 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 Machine Learning for Astrophysics written by Filomena Bufano and published by Springer Nature. This book was released on 2023-11-15 with total page 206 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book reviews the state of the art in the exploitation of machine learning techniques for the astrophysics community and gives the reader a complete overview of the field. The contributed chapters allow the reader to easily digest the material through balanced theoretical and numerical methods and tools with applications in different fields of theoretical and observational astronomy. The book helps the reader to really understand and quantify both the opportunities and limitations of using machine learning in several fields of astrophysics.

Download or read book Statistics Data Mining and Machine Learning in Astronomy written by Željko Ivezić and published by Princeton University Press. This book was released on 2019-12-03 with total page 548 pages. Available in PDF, EPUB and Kindle. Book excerpt: "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. The updates in this new edition will include fixing "code rot," correcting errata, and adding some new sections. In particular, the new sections include new material on deep learning methods, hierarchical Bayes modeling, and approximate Bayesian computation. 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"--

Download or read book Statistics Data Mining and Machine Learning in Astronomy written by Željko Ivezić and published by Princeton University Press. This book was released on 2014-01-12 with total page 550 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book Quasars at All Cosmic Epochs written by and published by . This book was released on 2018 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The last 50 years have seen a tremendous progress in the research on quasars. From a time when quasars were unforeseen oddities, we have come to a view that considers quasars as active galactic nuclei, with nuclear activity a coming-of-age experienced by most or all galaxies in their evolution. We have passed from a few tens of known quasars of the early 1970s to the 500,000 listed in the catalogue of the Data Release 14 of the Sloan Digital Sky Survey. Not surprisingly, accretion processes on the central black holes in the nuclei of galaxies -- the key concept in our understanding of quasars and active nuclei in general -- have gained an outstanding status in present-day astrophysics. Accretion produces a rich spectrum of phenomena in all bands of the electromagnetic spectrum. The power output of highly-accreting quasars has impressive effects on their host galaxies. All the improvement in telescope light gathering and in computing power notwithstanding, we still miss a clear connection between observational properties and theory for quasars, as provided, for example, by the H-R diagram for stars. We do not yet have a complete self-consistent view of nuclear activity with predictive power, as we do for main-sequence stellar sources. At the same time quasars offer many "windows open onto the unknown". On small scales, quasar properties depend on phenomena very close to the black hole event horizon. On large scales, quasars may effect evolution of host galaxies and their circum-galactic environments. Quasars' potential to map the matter density of the Universe and help reconstruct the Universe's spacetime geometry is still largely unexploited. The times are ripe for a critical assessment of our present knowledge of quasars as accreting black holes and of their evolution across the cosmic time. The foremost aim of this research topic is to review and contextualize the main observational scenarios following an empirical approach, to present and discuss the accretion scenario, and then to analyze how a closer connection between theory and observation can be achieved, identifying those aspects of our understanding that are still on a shaky terrain and are therefore uncertain knowledge. This research topic covers topics ranging from the nearest environment of the black hole, to the environment of the host galaxies of active nuclei, and to the quasars as markers of the large scale structure and of the geometry of spacetime of the Universe. The spatial domains encompass the accretion disk, the emission and absorption regions, circum-nuclear starbursts, the host galaxy and its interaction with other galaxies. Systematic attention is devoted to some key problems that remain outstanding and are clearly not yet solved: the existence of two quasar classes, radio quiet and radio loud, and in general, the systematic contextualization of quasar properties the properties of the central black hole, the dynamics of the accretion flow in the inner parsecs and the origin of the accretion matter, the quasars' small and large scale environment, the feedback processes produced by the black hole into the host galaxy, quasar evolutionary patterns from seed black holes to the present-day Universe, and the use of quasars as cosmological standard candles. The timing is appropriate as we are now witnessing a growing body of results from major surveys in the optical, UV X, near and far IR, and radio spectral domains. Radio instrumentation has been upgraded to linear detector -- a change that resembles the introduction of CCDs for optical astronomy -- making it possible to study radio-quiet quasars at radio frequencies. Herschel and ALMA are especially suited to study the circum-nuclear star formation processes. The new generation of 3D magnetohydrodynamical models offers the prospective of a full physical modeling of the whole quasar emitting regions. At the same time, on the forefront of optical astronomy, applications of adaptive optics to long-slit spectroscopy is yielding unprecedented results on high redshift quasars. Other measurement techniques like 2D and photometric reverberation mapping are also yielding an unprecedented amount of data thanks to dedicated experiments and instruments. Thanks to the instrumental advances, ever growing computing power as well as the coming of age of statistical and analysis techniques, the smallest spatial scales are being probed at unprecedented resolution for wide samples of quasars. On large scales, feedback processes are going out of the realm of single-object studies and are entering into the domain of issues involving efficiency and prevalence over a broad range of cosmic epochs. The Research Topic "Quasars at all Cosmic Epochs" collects a large fraction of the contributions presented at a meeting held in Padova, sponsored jointly by the National Institute for Astrophysics, the Padova Astronomical Observatory, the Department of Physics and Astronomy of the University of Padova, and the Instito de Astrofísica de Andalucía (IAA) of the Consejo Superiór de Investigación Cientifica (CSIC). The meeting has been part of the events meant to celebrate the 250th anniversary of the foundation of the Padova Observatory.

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 Clustering of High Redshift Quasars written by John D. Timlin (III) and published by . This book was released on 2018 with total page 268 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this work, we investigate the clustering of faint quasars in the early Universe and use the clustering strength to gain a better understanding of quasar feedback mechanisms and the growth of central supermassive black holes at early times in the history of the Universe. It has long been understood (e.g., Hopkins et al. 2007a) that the clustering of distant quasars can be used as a probe of different feedback models; however, until now, there was no sample of faint, high-redshift quasars with sufficient density to accurately measure the clustering strength. Therefore we conducted a new survey to increase the number density of these objects. Here, we describe the Spitzer -IRAC Equatorial Survey (SpIES) which is a moderately deep, large-area Spitzer survey which was designed to discover faint, high-redshift (2.9 = z = 5.1) quasars. SpIES spans ~115 deg^2 in the equatorial "Stripe 82" region of the Sloan Digital Sky Survey (SDSS) and probes to 5-[sigma] depths of 6.13 [mu]-Jy (21.93 AB magnitude) and 5.75 [mu]-Jy (22.0 AB magnitude) at 3.6 and 4.5 microns. At these depths, SpIES is able to observe faint quasars, and we show that SpIES recovers ~94% of the high-redshift (z 3.5), spectroscopically-confirmed quasars that lie within its footprint. SpIES is also ideally located on Stripe 82 for two reasons: It surrounds existing infrared data from the Spitzer-HETDEX Exploratory Large-area (SHELA) survey which increases the area of infrared coverage, and there is a wide range of multi-wavelength, multi-epoch ancillary data on Stripe 82 which we can use together to select high-redshift quasar candidates. To photometrically identify quasar candidates, we combined the optical data from the Sloan Digital Sky Survey and the infrared data from SpIES and SHELA and employed three machine learning algorithms. These algorithms were trained on the optical/infrared colors of known, high-redshift quasars. Using this method, we generate a sample of 1378 objects that are both faint (i 20.2) and high-redshift (2.9

Download or read book Modelling and Simulation in Science written by Vito Di Ges— and published by World Scientific. This book was released on 2007 with total page 350 pages. Available in PDF, EPUB and Kindle. Book excerpt: This proceedings volume contains results presented at the Sixth International Workshop on Data Analysis in Astronomy OCo OC Modeling and Simulation in ScienceOCO held on April 15-22, 2007, at the Ettore Majorana Foundation and Center for Scientific Culture, Erice, Italy. Recent progress and new trends in the field of simulation and modeling in three branches of science OCo astrophysics, biology, and climatology OCo are described in papers presented by outstanding scientists. The impact of new technologies on the design of novel data analysis systems and the interrelation among different fields are foremost in scientists'' minds in the modern era. This book therefore focuses primarily on data analysis methodologies and techniques. Sample Chapter(s). Chapter 1: Simulations for Uhe Cosmic Ray Experiments (562 KB). Contents: Astrophysics, Cosmology and Earth Physics: Simulations for UHE Cosmic Ray Experiments (J Knapp); Problems and Solutions in Climate Modeling (A Sutera); Statistical Analysis of Quasar Data and Validity of the Hubble Law (S Roy et al.); Quantum Astronomy and Information (C Barbieri); Biology, Biochemistry and Bioinformatics: From Genomes to Protein Models and Back (A Tramontano et al.); Exploring Biomolecular Recognition by Modeling and Simulation (R Wade); BioInfogrid: Bioinformatics Simulation and Modeling Based on Grid (L Milanesi); Methods and Techniques: Optimization Strategies for Modeling and Simulation (J Louchet); Biclustering Bioinformatics Data Sets: A Possibilistic Approach (F Masulli); From the Qubit to the Quantum Search Algorithms (G Cariolaro & T Occhipinti); Comparison of Stereo Vision Techniques for Cloud-Top Height Retrieval (A Anzalone et al.); and other papers. Readership: Physicists; biologists; computer scientists and data analysts."

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 Computer Vision and Applications written by Bernd Jahne and published by Academic Press. This book was released on 2000-04-24 with total page 704 pages. Available in PDF, EPUB and Kindle. Book excerpt: CD-ROM contains: Searchable version of text with hyperlinks.

Download or read book Mining the Sky written by A.J. Banday and published by Springer Science & Business Media. This book was released on 2001-09-11 with total page 732 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book reviews methods for the analysis of astronomical datasets, particularly emphasizing very large databases arising from both existing and forthcoming projects, as well as current large-scale computer simulation studies. Leading experts give overviews of cutting-edge methods applicable in the area of astronomical data mining.

Download or read book The Optical Variability of Quasars as Seen by Sloan Digital Sky Survey written by Chelsea Louise MacLeod and published by . This book was released on 2012 with total page 171 pages. Available in PDF, EPUB and Kindle. Book excerpt: I provide a quantitative analysis of the database of 3.5 million photometric measurements for 80,000 spectroscopically confirmed quasars recently assembled by the Sloan Digital Sky Survey (SDSS). This database is an excellent data set to study quasar variability, and includes 9,000 well-sampled light curves from Stripe 82 and a 25,000-object two-epoch sample from the northern Galactic sky. I show that the damped random walk model provides a robust statistical description of these data. This model characterizes the variability using two parameters, the long-term amplitude and characteristic timescale, and these model parameters show trends with physical parameters such as the luminosity and black hole mass. This mathematical model supports accretion disk instabilities as the dominant variability mechanism. However, there is substantial scatter around the mean trends, as other sources of stochasticity are yet to be discovered. If magnetohydrodynamic models could be improved to reproduce the observed characteristics of variability, they should be able to shed light on the source of stochasticity inherent in quasar light curves. In this dissertation, I discuss the application of a quasar variability model to study the underlying physics of accretion disks. I also discuss the prospect of using variability as a selection method for quasars. Lastly, I use results from SDSS to discuss the prospects of studying quasar variability in upcoming large time-domain sky surveys.

Download or read book Deep Learning in Solar Astronomy written by Long Xu and published by Springer Nature. This book was released on 2022-05-27 with total page 103 pages. Available in PDF, EPUB and Kindle. Book excerpt: The volume of data being collected in solar astronomy has exponentially increased over the past decade and we will be entering the age of petabyte solar data. Deep learning has been an invaluable tool exploited to efficiently extract key information from the massive solar observation data, to solve the tasks of data archiving/classification, object detection and recognition. Astronomical study starts with imaging from recorded raw data, followed by image processing, such as image reconstruction, inpainting and generation, to enhance imaging quality. We study deep learning for solar image processing. First, image deconvolution is investigated for synthesis aperture imaging. Second, image inpainting is explored to repair over-saturated solar image due to light intensity beyond threshold of optical lens. Third, image translation among UV/EUV observation of the chromosphere/corona, Ha observation of the chromosphere and magnetogram of the photosphere is realized by using GAN, exhibiting powerful image domain transfer ability among multiple wavebands and different observation devices. It can compensate the lack of observation time or waveband. In addition, time series model, e.g., LSTM, is exploited to forecast solar burst and solar activity indices. This book presents a comprehensive overview of the deep learning applications in solar astronomy. It is suitable for the students and young researchers who are major in astronomy and computer science, especially interdisciplinary research of them.

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 Data Analysis in Astronomy II written by V. di Gesù and published by Springer Science & Business Media. This book was released on 2013-03-09 with total page 389 pages. Available in PDF, EPUB and Kindle. Book excerpt: The II international workshop on "Data Analysis in Astronomy" was intended to provide an overview on the state of the art and the trend in data analy sis and image processing in the context of their applications in Astronomy. The need for the organization of a second workshop in this subject derived from the steady. growing and development in the field and from the increasing cross-interaction between methods, technology and applications in Astronomy. The book is organized in four main sections: - Data Analysis Methodologies - Data Handling and Systems dedicated to Large Experiments - Parallel Processing - New Developments The topics which have been selected cover some of the main fields in data analysis in Astronomy. Methods that provide a major contribution to the physical interpretation of the data have been considered. Attention has been devoted to the description of the data analysis and handling organization in very large experiments. A review of the current major satellite and ground based experiments has been included. At the end of the book the following 'Panel Discussions' are included: - Data Analysis Trend in Optical and Radio Astronomy - Data Analysis Trend in X and Gamma Astronomy - Problems and Solutions in the Design of Very Large Experiments - Trend on Parallel Processing Algorithms These contributions in a sense summarize the 'live' reaction of the audience to the various topics.

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.