21CMAPAST data analysis

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21CMA/PAST data analysis

• Ue-Li Pen ???
• Chris Hirata

Xiang-Ping Wu ???, Jeff Peterson
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Reionization

• First objects
• 21cm _at_ z6
• 50-200 Mhz
• ?T 23 mK, 0.3 mJy
• Angular scale 5

z10 simulation, Furlanetto et al, 2004
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Foreground Synchrotron
408 MHz Haslam
Much brighter than signal, but no spectral
structure
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Detectability

• Luminosity proportional to object volume bigger
  structures easier to find
• Noise dominated by galaxy T300(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).

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21CMA/PAST Site
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21CMA/PAST Strategy

• Fast track to data avoid custom design,
  off-the-shelf only.
• Use existing TV technology, commodity PCs for
  correlations
• Learn as you build fast turnaround, flexibility

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Antenna Design

• Noise dominated by galaxy
  Tgal280 (150Mhz/f)2.5 K _at_ NCP
• sensitivity 104 m2 effective area
• Resolution aperture synthesis,80 elements, 3km
  baselines
• Receiver noise NF
• Pointing at north celestial pole, elevation 43o
• simple,fast?Currently 23 hexagonal pods, 12
  correlating

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Ulastai
Urumqi 150 km
42º 55N 86º 45 E elev 2600m
Ustir station
Ground shield5000m mountains on all sides
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Software correlator
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U-V map data
Almost no interference, excellent u-v coverage
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Protype data, Feb, 2005
12 working pods of 127 antenna each
NCP
3C061.1
100-200 Mhz, 10o FOV
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CMB Analogy

• Searching for very low surface brightness sources
• Potentially severe foregrounds
• Fully sampled u-v planes different from CLEANing
• Statistics of noise and foregrounds can be
  described very accurately
• Large Field of view planar assumption breaks.
  WMAP 120 deg difference map

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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.

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Data Flow

• raw time stream
• Optimal map construction to reduce data size
  Deconvolution, Wiener, etc
• Foreground removal
• Noise covariance matrix
• Power spectrum
• Window functions

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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

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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)
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Computational Complexity

• O(N3) not tractable for all sky, workable for
  small fields at low resolution, up to 105 pixels
• Accelerated plans in development Conjugate
  gradient, multigrid (e.g. Pen 2004) as used in
  lensing and CMB analysis

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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