deconvolution /source subtraction via forward modeling ( pindor et al., 2011, pasa, 28, 46)
DESCRIPTION
Deconvolution /source subtraction via forward modeling ( Pindor et al., 2011, PASA, 28, 46) (GB et al., 2011, MNRAS, 413, 411) It can now run on the instrumental polarization -> speed increases proportional to the length of the integration It accounts for the pixel noise statistics. - PowerPoint PPT PresentationTRANSCRIPT
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Deconvolution/source subtraction via forward modeling
(Pindor et al., 2011, PASA, 28, 46)(GB et al., 2011, MNRAS, 413, 411)
•It can now run on the instrumental polarization -> speed increases proportional to the length of the integration
•It accounts for the pixel noise statistics
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Deconvolving real data: an example
Source J0523-36 is modeled in the same
way that the pointing is processed via the RTS
(beams, cadence, frequency)
Convergence after 2 iterations. Positional
error ~ 15’, flux error ~ 10%
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Primary beam measurements
• the sky drifts overhead while the tiles point at zenith;
• ~30 bandwidth centered @ 185 MHz;
• snapshot images (one every 5 min) are used to measure the beam response towards the J0444-2905 (which is ~ 37 Jy @ 185 MHz);
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Primary beam measurements
J0444-2905
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Fitting a simple primary beam model
The beam is accurate at a 2% leveland predicts the source fluxex with 5% rms accuracy
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The rms is 0.63 Jy/beam
Stokes I
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The rms is 41 mJy/beam
Stokes Q
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The rms is 28 mJy/beam
Stokes U
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Extending the beam work: zooming in to HydA field
HydA
Observations span slightly more than 5 hours (total) over 110-200 MHz:21 tiles available
HydA provides the direction independent calibration of the arraySnapshot images co-added
Multi-frequency synthesis (but in the image plane)
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to be continued
with existing data
with X16