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The Atmospheric Emission Signal as seen with SHARC-II

Alexander van Engelen

University of British Columbia

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Who am I?

• Working with:– Douglas Scott (UBC)– Andy Gibb (UBC)– Tim Jenness (JAC, Hilo HI)– Dennis Kelly (UK ATC, Edinburgh)

on the data reduction pipeline software for

SCUBA-2 Scanmap research

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Introduction• In sub-mm experiments, atmospheric emission due to

water vapor is the strongest component of the data– Bright– Varies on long spatial and temporal scales

• For data reduction studies it is important to model this in a useful and accurate way

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Current Model

• Use a fluid dynamic model (Kolmogorov) to describe the characteristics of the signal

• In SCUBA-2 simulations the emission from various altitudes is approximated by a single, constant screen of emission – Gaussian 2-D random field– Fixed at an altitude of ~800 m, and blows past

the observatory at the local wind speed

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Atmospheric Emission Image

• Features here are very large

• This constant screen blows past the observatory at ~15 m/s (~5000 arcsec/sec)

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SCUBA-2 scanmap basics

• Simple raster scan• To fill in the under-

sampled 450μm array, scan at an angle of arctan(1/2)≈26.5° to array axes

(courtesy D, Kelly)

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SCUBA-2 scanmap simulation

• Simulated scan over a regular 2-d array of point sources

• Just a simple reprojection of the time series onto a map – nothing fancy here!

• Note streaks due to atmospheric emission signal

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Issues

• Is this truly a Gaussian random field?• Power spectrum?• Constant wind vector?• Component on scales smaller than the array?• How stable are the properties of the screen?

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• In order to learn more about the properties of this signal Colin Borys kindly gave us some SHARC-II data

• Lissajous scan of MS0451

– considered to be faint enough that source flux can be neglected here

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• The data is overwhelmingly common-mode across the array

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Sample array-mean signals

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Sample spectra

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Model predicts (in the timestream)

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Animation

From a data reduction perspective we are interested in whether the atmosphere is completely described by a common-mode signal.

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Residual after a common-mode

signal is subtracted away

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Residual after common-mode subtraction

Still some correlated structures remaining

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Zero-timelag cross-correlations in

residuals

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ttbbtrrbbCσσ

=

-Difficult to understand

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Direct detection of Kolmogorov model? (I)

• Compare derivative of array mean with slope of fit plane across the array; positive correlation indicates a comfirmation

• It turns out that for the SHARC-II data the gradient is overwhelmed by instumental effects.

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Direct detection of Kolmogorov model? (II)

• There should be a shift in the signal (of a fraction of a sample) between detectors if the wind speed is reasonable

• Unfortunately it is difficult to measure this explicitly.

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Conclusions

• Since the data is so common-mode, subtracting a simple mean is not ruled out as a way of dealing with the atmospheric emission signal. However, there seem to be some small-amplitude correlated structures that remain.

• No direct detection of the wind-blown screen model was made in SHARC-II data.

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(Fin)