I Skydips

The skydip observing mode measures the sky brightness at a range of elevations and uses that data to calculate the zenith sky opacity. The absolute value of the sky brightness is required and this values is calculated by interpolating its measured signal from that measured with ambient and cold loads.

In order to calculate the zenith sky opacity to the sky brightnesses the skydip task fits a theoretical curve to the data. The theoretical curve at each wavelength takes the form:

where $J_{\mathrm{meas}}$ is the measured brightness temperature of the sky, ${\eta }_{\mathrm{tel}}$ is the transmission of the telescope, $J_{\mathrm{tel}}$ is the brightness temperature of a black-body at the temperature of the telescope, $J_{\mathrm{atm}}$ is the brightness temperature of the atmosphere, $b$ is the bandwidth factor of the filter being used ($1-b$ is the fraction of the filter bandwidth that is opaque due to atmospheric absorption and, like $\tau$, it is a function of water vapour content), $\tau$ is the zenith sky optical depth and $A$ is the airmass of the measurement.

Of these parameters, $J_{\mathrm{meas}}$, $J_{\mathrm{tel}}$ and $A$ are known. $J_{\mathrm{atm}}$ can be estimated from the ambient air temperature at ground level using a model for the behaviour of the observing layer above the telescope, as described below. ${\eta }_{\mathrm{tel}}$ may be fitted to the data for every skydip and, because it does not vary with atmospheric conditions, a reliable ‘average’ value can be derived from many observations. Thus, there are two remaining free parameters, $\tau$ and $b$, that must be derived from the fit (three if fitting ${\eta }_{\mathrm{tel}}$).

$J_{\mathrm{atm}}$ is calculated from $T_{\mathrm{amb}}$, the ambient air temperature, by assuming that the sky emission is dominated by a single absorber/emitter whose density falls exponentially and temperature linearly with height. In this case it can be shown that

where $h_1$ is $J_{\mathrm{amb}/6.5}$ to give a 6.5 K fall in temperature per km height, $h_2$ is the scale height of the absorbers (2 km), $A$ is the airmass and $k$ the extinction per km.

If we approximate the result of the integral by

it can be shown that $X_{\mathrm{g}}$ has the form

where $T_{\mathrm{lapse}}$ is the temperature drop per kilometre altitude ($-6.5$ K/km) and $X_{\mathrm{gconst}}$ is a constant determined empirically and has a value of 3.669383.

For more information see [35].

I.1 Calibration

The choice for T_HOT and T_COLD critically affects the result of the skydip fit. The default values for the hot and cold temperatures are usually stored in the data header but occasionally these values are redetermined and the header values must be over-ruled. As of version 1.6 of Surf the cold load temperature (as well as the default telescope efficiency, ${\eta }_{\mathrm{tel}}$) for the 850 and 450-$\mu$m filters is suggested from a lookup table rather than the data headers. Also, the hot load temperature is now known to be wavelength dependent and an adjustment of -1K (at 850 microns) and -3K (at 450 microns) is now automatically applied to the value stored in the header. More details on skydip calibration can be found in Archibald et al [36].

I.2 Removing bad skydip data from the fit

Occasionally it is necessary to remove bad points from skydip data prior to fitting. This is implemented in the same way as it is implemented for other SCUBA data by using change_quality. The following extra steps are required:

(1)

Run reduce_switch to calculate the sky brightness temperature for each integration at each airmass (measurement). The cold load temperature for each sub instrument will be requested.

  % reduce_switch 70
  SURF: Opening 19971115_dem_0070 in /scuba/observe/19971115/dem
  SURF: run 70 was a SKYDIP observation
  SURF: file contains data for 1 switch(es) in 1 exposure(s) in 10 integration(s)
  in 10 measurement(s)
  OUT - Name of output file to contain reduced switch data /’o70’/ >
  T_COLD - Temperature of cold load for SHORT_DC /95/ >
  T_COLD - Temperature of cold load for LONG_DC /55/ >

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Figure 15: Skydip data after processing with reduce_switch (left) and after measurement 5 has been removed with change_quality (right).

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(2)

The resulting output file looks just like a file produced by reduce_switch on map data: it contains a 2 dimensional data array of sub-instrument (bolometer) number along the first axis and sample number (number of integrations times number of measurements) along the second axis. You can find the sub-instrument corresponding to each ‘bolometer’ number either by running skydip and noting the order of the listed sub-instruments or by using the Kappa fitslist command:

  % fitslist o70 | grep SUB_
  SUB_1   = ’SHORT   ’           / SCUBA instrument being used
  SUB_2   = ’LONG    ’           / SCUBA instrument being used
  SUB_3   = ’not used’           / SCUBA instrument being used
  SUB_4   = ’not used’           / SCUBA instrument being used
  SUB_5   = ’not used’           / SCUBA instrument being used

For example, the data for the second sub-instrument (in this case the LONG array) can be plotted by using:

  % linplot mode=2 device=xwindows ’o70(2,)’

Fig. 15 shows an example. Note that, in contrast with other observing modes, the second axis is labelled in measurements rather than integrations.

(3)

Once a bad measurement has been identified, it can be switched off using change_quality:

  % change_quality ’o70{b2;m5}’ yes
  SURF: run 70 was a SKYDIP observation of not used
  SURF: file has data for 2 bolometers, measured at 100 positions.
   - there are data for 1 exposure(s) in 10 integration(s) in 10 measurements.

The main thing here is that the m identifier should be used to specify measurements¹⁵ and that only bolometer (i.e. sub-instrument) 2 should be affected.

(4)

Now skydip(or sdip) can be run on the file:

  % skydip o70
  SURF: run 70 was a SKYDIP observation
  SURF: observation started at sidereal time 1 10 41 and ended at 1 16 38
  SURF: file contains data for the following sub-instrument(s)
   - SHORT with filter 350
   - LONG with filter 750
  SUB_INSTRUMENT - Name of sub-instrument to be analysed /’SHORT’/ > long
  SURF: file contains data for 10 integration(s) in 10 measurement(s)
  ETA_TEL - Telescope efficiency /0.87/ >
  B_VAL - B parameter /-1/ >
  SCULIB: fit for filter 750 and sub-instrument LONG_DC
   eta =  0.87 +/-  0.00  b =  0.86 +/-  0.01  tau =   0.667 +/- 0.007
   Standard Deviation of fit residual =   0.81 K (X=     0.9 N=    7)

The fit is shown in Fig. 16. skydip is the only task that can process raw demodulated data and data processed with reduce_switch.

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Figure 16: Skydip plus model without measurement 5

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