Big Data in Astronomy

Scientific Data Processing for Advanced Radio Telescopes

Linghe Kong (Redaktør) ; Tian Huang (Redaktør) ; Yongxin Zhu (Redaktør) ; Shenghua Yu (Redaktør)

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. Les mer
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Vår pris: 2076,-

(Paperback) Fri frakt!
Leveringstid: Sendes innen 21 dager
På grunn av Brexit-tilpasninger og tiltak for å begrense covid-19 kan det dessverre oppstå forsinket levering.

Om boka

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.

Fakta

Innholdsfortegnelse

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