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  • 1. 21CMA/ PAST data analysis Ue-Li Pen 彭威礼 Chris Hirata Xiang-Ping Wu 武向平 , Jeff Peterson
  • 2. Reionization
    • First objects:
    • 21 cm @ z>6
    • 50-200 Mhz
    • Δ T = 23 mK, ~0.3 mJy
    • Angular scale 5’< Θ < 20 ’ , freq res 500 khz
    z=10 simulation, Furlanetto et al, 2004
  • 3. Foreground: Synchrotron 408 MHz Haslam Much brighter than signal, but no spectral structure
  • 4. Detectability
    • Luminosity proportional to object volume: bigger structures easier to find
    • Noise dominated by galaxy: T=300(f/150 Mhz) -2.5 , higher frequency (lower redshift) much easier
    • Mean emission very hard to discern (Gnedin and Shaver 2004).
    • First targets: Stromgren spheres around bright quasars (Wyithe and Loeb 2004).
  • 5. 21CMA/ PAST Site
  • 6. 21CMA/ PAST Strategy
    • Fast track to data: avoid custom design, off-the-shelf only.
    • Use existing TV technology, commodity PC’s for correlations
    • Learn as you build: fast turnaround, flexibility
  • 7. Antenna Design
    • Noise dominated by galaxy: T gal =280 (150Mhz/f) 2.5 K @ NCP
    • sensitivity : 10 4 m 2 effective area
    • Resolution: aperture synthesis , 80 elements , 3km baselines
    • Receiver noise: NF < 2 dB ( T <200K)
    • Pointing at north celestial pole, elevation 43 o
    • simple , fast 。 Currently 23 hexagonal pods , 12 correlating
  • 8. Ulastai Ustir station 42º 55’ N 86º 45’ E elev 2600m Urumqi 150 km Ground shield : 5 000 m mountains on all sides
  • 9.  
  • 10.  
  • 11. Software correlator
  • 12. U-V map data Almost no interference, excellent u-v coverage
  • 13. Protype data, Feb, 2005 12 working pods of 127 antenna each 100-200 Mhz, 10 o FOV 3C061.1 NCP
  • 14. CMB Analogy
    • Searching for very low surface brightness sources
    • Potentially severe foregrounds
    • Fully sampled u-v planes: different from CLEAN ing
    • Statistics of noise and foregrounds can be described very accurately
    • Large Field of view: planar assumption breaks. WMAP: 120 deg difference map
  • 15. CMB map making
    • Linear algebra approach to map making
    • Used by most experiments, including WMAP, Planck, Boomerang, DASI, CBI
    • Exactly solvable for Gaussian random fields
    • Noise properties fully characterized
    • Computationally expensive
    • Fast workarounds: CG, multigrid, etc.
  • 16. Data Flow
    • raw time stream
    • Optimal map construction to reduce data size: Deconvolution, Wiener, etc
    • Foreground removal
    • Noise covariance matrix
    • Power spectrum
    • Window functions
  • 17. Analysis procedure
    • Calibrate system from celestial sources
    • Determine beam from sky
    • Generalized BEAM contains all processes between source and data: ISM, ionosphere, antenna, polarization, transmission line, etc.
    • Wiener filtered map
  • 18.  
  • 19. Same for polarization: consider all polarization to be noise, solve for I map. One needs to know the beam accurately! Varies with time, frequency, position on sky, position of antenna, ionosphere, instrument. Calibration from bright point sources (Hirata)
  • 20. Computational Complexity
    • O(N 3 ): not tractable for all sky, workable for small fields at low resolution, up to 10 5 pixels
    • Accelerated plans in development: Conjugate gradient, multigrid (e.g. Pen 2004) as used in lensing and CMB analysis
  • 21. Conclusions
    • Linear map making theory well understood from CMB analysis, optimal algorithms for Gaussian fields, even full sky.
    • Minimum signal-to-noise deconvolved foreground subtraction with Wiener filters, implementation on real data in progress