Big Data in Astronomy
Scientific Data Processing for Advanced Radio Telescopes

Coordinators: Kong Linghe, Huang Tian, Zhu Yongxin, Yu Shenghua

Language: English

155.28 €

In Print (Delivery period: 14 days).

Add to cartAdd to cart
Publication date:
438 p. · 19x23.3 cm · Paperback

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.

Part A: Fundamentals

Chapter 1: Introduction of Radio Astronomy

Chapter 2: Fundamentals of Big Data in Radio Astronomy

Part B: Big Data Processing

Chapter 3: Pre-processing Pipeline on FPGA

Chapter 4: Real-time stream processing in radio astronomy

Chapter 5: Digitization, Channelization and Packeting

Chapter 6: Processing Data of Correlation on GPU

Chapter 7: Data Calibration for single dish radio telescope

Chapter 8: Imaging Algorithm Optimization for Scale-out Processing

Part C: Computing Technologies

Chapter 9: Execution Framework Technology

Chapter 10: Application Design For Execution Framework

Chapter 11: Heterogeneous Computing Platform for Backend Computing Tasks

Chapter 12: High Performance Computing for Astronomical Big Data

Chapter 13: Spark and Dask Performance Analysis Based on ARL Image Library

Chapter 14: Applications of Artificial Intelligence in Astrnomical Big Data

Part D: Future Developments

Chapter 15: Mapping the Universe with 21cm Observations

Practitioners and researchers working in data processing for astronomy; students studying data in astronomy
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 Da
  • 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)