Big data in astronomy : scientific data processing for advanced radio telescopes /

Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: Kong, Linghe (Computer scientist) (Editor ), Huang, Tian (Computer scientist) (Editor ), Zhu, Yongxin (Computer scientist) (Editor ), Yu, Shenghua (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Amsterdam, Netherlands : Elsevier, [2020]
Temas:
Radio astronomy > Data processing.
Big data.
Radioastronomie > Informatique.
Donn�ees volumineuses.
Big data
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Intro
  • Big Data in Astronomy: Scientific Data Processing for Advanced Radio Telescopes
  • Copyright
  • Contents
  • Contributors
  • Preface
  • Acknowledgments
  • Part A: Fundamentals
  • Chapter 1: Introduction to radio astronomy
  • 1. The history of astronomy
  • 1.1. Ancient astronomy
  • 1.2. Astronomy from the mid-16th century to the mid-19th century
  • 1.3. Astronomy since the mid-19th century
  • 2. What is radio astronomy
  • 2.1. How does radio astronomy occur
  • 2.2. The radio stars, quasars, and black holes
  • 2.2.1. The strongest radio source, Cygnus A, in the sky
  • 2.2.2. The discovery of cliff allergens and radio galaxies
  • 2.2.3. Nonthermal radiation
  • 2.2.4. Synchronous radiation
  • 2.2.5. Synchrotron radiation pattern
  • 2.2.6. Connect nonthermal radiation and cosmic rays
  • 2.2.7. Astrophysics of cosmic rays
  • 2.2.8. Discovery of quasars
  • 2.3. The radio astronomy instrument: Radio telescope
  • 2.4. Some achievements of radio astronomy
  • 2.5. Astronomical research nowadays
  • 3. Advanced radio telescope
  • 3.1. The square kilometer array (SKA)
  • 3.2. Fast
  • 4. The challenge of radio astronomy
  • 4.1. System noise
  • 4.2. Antennas and collecting area
  • 4.3. Data transmission
  • 5. The development tendency of radio astronomy
  • 5.1. Mid-frequency aperture arrays
  • 5.2. Entering a near future
  • References
  • Chapter 2: Fundamentals of big data in radio astronomy
  • 1. Big data and astronomy
  • 1.1. Background of big data
  • 1.2. Definitions and features of big data
  • 1.3. Development of big data
  • 1.4. Big data in astronomy
  • 1.5. Statistical challenges in astronomy
  • 2. Increasing data volumes of telescopes
  • 2.1. Sloan digital sky survey
  • 2.2. Visible and infrared survey telescope for astronomy
  • 2.3. Large synoptic survey telescope
  • 2.4. Thirty meter telescope
  • 3. Existing methods for the value chain of big data
  • 3.1. Data generation
  • 3.2. Data acquisition
  • 3.3. Data storage
  • 3.4. Data analysis
  • 3.4.1. Traditional data analysis methods
  • 3.4.2. Big data analytic methods
  • 3.4.3. Architecture for big data analysis
  • 4. Current statistical methods for astronomical data analysis
  • 4.1. Nonparametric statistics
  • 4.2. Data smoothing
  • 4.3. Multivariate clustering and classification
  • 4.4. Nondetections and truncation
  • 4.5. Spatial point processes
  • 5. Platforms for big data processing
  • 5.1. Horizontal scaling platforms
  • 5.2. Vertical scaling platforms
  • 5.2.1. High performance computing (HPC) clusters
  • 5.2.2. Multicore CPU
  • 5.2.3. Graphics processing unit (GPU)
  • 5.2.4. Field programmable gate arrays (FPGA)
  • References
  • Part B: Big data processing
  • Chapter 3: Preprocessing pipeline on FPGA
  • 1. FPGA interface for ADC
  • 1.1. ADC interleaving
  • 1.2. Bit alignment
  • 1.3. Stream deserialization
  • 2. FIR filtering
  • 2.1. Leakage
  • 2.2. Scalloping loss
  • 2.3. Polyphase filter
  • 3. Time-frequency domain transposing
  • 3.1. Real-valued FFT.

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