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E-raamat: Big Data in Astronomy: Scientific Data Processing for Advanced Radio Telescopes

Edited by (Research Professor, Shanghai Jiao Tong University, China), Edited by (Associate Profes), Edited by (Research Associate, Astrophysics Group, University of Cambridge, UK), Edited by (Professor, Shanghai Advanced Research Institute, Chinese Academy of Sciences, China)
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  • Ilmumisaeg: 13-Jun-2020
  • Kirjastus: Elsevier Science Publishing Co Inc
  • Keel: eng
  • ISBN-13: 9780128190852
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  • Formaat: PDF+DRM
  • Ilmumisaeg: 13-Jun-2020
  • Kirjastus: Elsevier Science Publishing Co Inc
  • Keel: eng
  • ISBN-13: 9780128190852

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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)
Contributors xi
Preface xiii
Acknowledgments xv
Part A Fundamentals
Chapter 1 Introduction to radio astronomy
3(26)
Jinlin Tan
Linghe Kong
1 The history of astronomy
3(2)
2 What is radio astronomy
5(10)
3 Advanced radio telescope
15(5)
4 The challenge of radio astronomy
20(5)
5 The development tendency of radio astronomy
25(2)
References
27(2)
Chapter 2 Fundamentals of big data in radio astronomy
29(32)
Jiale Lei
Linghe Kong
1 Big data and astronomy
29(8)
2 Increasing data volumes of telescopes
37(3)
3 Existing methods for the value chain of big data
40(10)
4 Current statistical methods for astronomical data analysis
50(2)
5 Platforms for big data processing
52(6)
References
58(3)
Part B Big data processing
Chapter 3 Preprocessing pipeline on FPGA
61(22)
Tian Huang
Yongxin Zhu
Yu Zheng
1 FPGA interface for ADC
61(4)
2 FIR filtering
65(4)
3 Time-frequency domain transposing
69(4)
4 Correlators based on FPGA
73(2)
5 General architectures for data reduction design and implementation
75(5)
6 Conclusion
80(1)
References
80(3)
Chapter 4 Real-time stream processing in radio astronomy
83(30)
Danny C. Price
1 Introduction
83(1)
2 Stream processing
84(2)
3 Heterogeneous signal processing
86(4)
4 Ethernet interconnect
90(5)
5 First-stage data processing
95(2)
6 Data redistribution
97(3)
7 Second-stage processing
100(7)
8 Discussion
107(2)
Acknowledgments
109(1)
References
109(4)
Chapter 5 Digitization, channelization, and packeting
113(26)
Dongliang Liu
Shenghua Yu
1 Digitization
113(6)
2 Channelization
119(7)
3 Packeting
126(7)
References
133(6)
Chapter 6 Processing data of correlation on GPU
139(26)
Yongxin Zhu
Junjie Hou
Yuefeng Song
Yu Zheng
Tian Huang
Huaiguang Wu
1 Introduction
139(3)
2 GPU-based cross-correlator engines
142(3)
3 Applying and implementing gridding algorithm after cross-correlator
145(10)
4 Applying and implementing deconvolution algorithm and parallel implementation after cross-correlator
155(6)
5 Summary
161(1)
References
161(4)
Chapter 7 Flux calibration for single-dish radio telescopes
165(20)
Bin Liu
Shenghua Yu
1 Basic concepts
165(2)
2 Flux calibration
167(1)
3 Processing spectral line data
168(9)
4 Observations of a brown dwarf by Arecibo single dish
177(6)
References
183(2)
Chapter 8 Imaging algorithm optimization for scale-out processing
185(30)
Haoyang Ye
Peter Hague
Stephen F. Gull
Sze Meng Tan
Bojan Nikolic
1 Imaging process
185(6)
2 Gridding and degridding
191(6)
3 The choice of the gridding function in the era of big data
197(10)
4 Bayesian source discrimination
207(3)
References
210(5)
Part C Computing technologies
Chapter 9 Execution framework technology
215(30)
Ying Mei
Rodrigo lobar
Chen Wu
Hui Deng
Shoulin Wei
Feng Wang
1 Introduction
215(2)
2 OpenCluster
217(7)
3 DALiuGE
224(18)
Acknowledgments
242(1)
References
242(3)
Chapter 10 Application design for execution framework
245(26)
Ying Mei
Rodrigo Tobar
Chen Wu
Hui Deng
Shoulin Wei
Feng Wang
1 OpenCluster applications design
245(3)
2 MUSER pipeline using OpenCluster
248(4)
3 Design CHILES on AWS using DALiuGE
252(2)
4 The migration of SAGECal/MPI to DALiuGe
254(14)
Acknowledgments
268(1)
References
268(1)
Further reading
269(2)
Chapter 11 Heterogeneous computing platform for backend computing tasks
271(34)
Yongxin Zhu
Tian Huang
Junjie Hou
Sen Du
Shijin Song
1 Introduction
271(1)
2 Computing architecture and platform
272(9)
3 Algorithm benchmarking
281(13)
4 Telescopes and applications
294(4)
5 Conclusion
298(1)
References
298(4)
Further reading
302(3)
Chapter 12 High-performance computing for astronomical big data
305(20)
Yongxin Zhu
Haihang You
Junjie Hou
Yu Zheng
Tian Huang
Yuefeng Song
1 Introduction
305(2)
2 Execution framework and prototype test
307(10)
3 Improving SKA algorithm reference library on high-performance computing platform
317(5)
4 Summary
322(1)
References
322(3)
Chapter 13 Spark and dask performance analysis based on ARL image library
325(22)
Kaiyu Fu
Qiuhong Li
Siyu Fan
Ting Li
Tian Huang
Yuan Luo
1 Introduction
325(2)
2 Preliminaries and notations
327(4)
3 Experiment
331(7)
4 Task scheduling based on data processing capacity
338(4)
5 Network connection model and routing topology model
342(2)
6 Conclusion
344(1)
References
345(2)
Chapter 14 Applications of artificial intelligence in astronomical big data
347(32)
Yatong Chen
Rui Kong
Linghe Kong
1 Introduction
347(1)
2 Machine learning for astronomical data calibration and repair
348(1)
3 Artificial intelligence algorithms in astronomy data classification and preprocessing
349(14)
4 Artificial intelligence application in astronomy data analysis
363(10)
5 Conclusion
373(1)
References
373(6)
Part D Future developments
Chapter 15 Mapping the universe with 21cm observations
379(28)
Xuelei Chen
1 The neutral hydrogen and 21 cm line
379(6)
2 The 21 cm experiments
385(10)
3 Data processing
395(8)
4 Conclusion
403(2)
References
405(1)
Further reading
406(1)
Index 407
Linghe Kong is currently a Research Professor in Department of Computer Science and Engineering at Shanghai Jiao Tong University and an engineer in the scientific data processing group in SKA China. Before that, he was a postdoctoral researcher at Columbia University and McGill University. He received his Ph.D. degree from Shanghai Jiao Tong University, China, his Masters degree from TELECOM SudParis, France, and his B. E. degree from Xidian University, China. His research interests include big data, Internet of things, and mobile computing systems. He has published more than 60 papers in refereed journals and conferences, such as ACM MobiCom, IEEE INFOCOM, IEEE RTSS, IEEE ICDCS, IEEE TMC, and IEEE TPDS. He serves on the editorial boards of several journals including Springer Telecommunication Systems and KSII Transactions on Internet and Information Systems. He organized several special issues such as in IEEE Communications Magazine and in the Computer Journal. He is a senior member of IEEE. Tian Huang is Research Associate of the Astrophysics Group, Cavendish Lab, University of Cambridge. He takes part in multiple radio telescope array projects and mainly focuses on data preprocessing and quality metrics. In March 2016, he graduated from the School of Microelectronics at Shanghai Jiao Tong University, where he completed his PhD thesis. His main research interest is Data Mining for time series, including time series big data indexing, anomaly detecting, and computer architecture for time series data mining and statistical models for time series data. He has published 9 SCI journal and 18 EI conference papers. He has rich experience on software and hardware co-designing. Yongxin Zhu is a full Professor at Shanghai Advanced Research Institute, Chinese Academy of Sciences (CAS). He is also an Adjunct Professor with the School of Microelectronics at the Shanghai Jiao Tong University (SJTU). He is currently the technical leader of Chinese Consortium of Science Data Processor (SDP) for Square Kilometre Array Telescope. He has published over 130 English journal and conference papers, 40 Chinese journal papers and 20 China patent approvals in the areas of computer architecture, embedded systems, and big data processing. With around 1,000 citations of these works in recent years, he has received recognition in China and Asia with IEEE best paper award, Shanghai innovation award, SJTU Annual Outstanding Teacher Award and Bilingual Teaching Award. To date, he has received around 20 million RMB in grants from various funding agencies and industrial partners in China. Prior to his tenure with CAS and SJTU, he worked as a research fellow with the National University of Singapore in 2002-2005, a senior consultant with S1 Incorporation (inventor of the 1st Internet banking in the world) in 1999-2002 and a teaching assistant with the Department of Computer Science and Engineering, SJTU in 1994-1995. He is a guest editor of Journal of Systems Architecture, senior member of IEEE and China Computer Federation (CCF). He has been also a Visiting Professor with National University of Singapore since 2013. Shenghua Yu is an Associate Professor at National Astronomical Observatories, Chinese Academy of Sciences (CAS). He received his PhD degree in astrophysics from Queens University of Belfast in 2012, and worked as a post-doc fellow at University of Western Australia in 2014-2015. His main research interests include gravitational wave astrophysics and astronomy, double compact objects, radio emission from ultracool dwarfs and radiation mechanisms. He has published ~13 SCI journal papers, 3 Chinese journal and conference papers, 2 China patent approvals, and 3 China computer software copyrights in the research areas.