1. 1 The Atmospheric Emission Signal as seen with SHARC-II Alexander van Engelen University of British Columbia

2. 2 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

3. 3 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

4. 4 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

5. 5 Atmospheric Emission Image Features here are very large This constant screen blows past the observatory at ~15 m/s (~5000 arcsec/sec)

6. 6 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)

7. 7 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

8. 8 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?

9. 9 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

10. 10 The data is overwhelmingly common-mode across the array

11. 11 Sample array-mean signals

12. 12 Sample spectra Model predicts (in the timestream)

13. 13 Animation From a data reduction perspective we are interested in whether the atmosphere is completely described by a common-mode signal.

14. 14 Residual after a common-mode signal is subtracted away

15. 15 Residual after common-mode subtraction Still some correlated structures remaining

16. 16 Zero-timelag cross- correlations in residuals -Difficult to understand

17. 17 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.

18. 18 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.

19. 19 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.

20. 20 (Fin)