Sky noise removal in scan maps

There are no sky bolometers in a scan map, i.e. each bolometer can be on source, and furthermore a bolometer will cover a different region of the sky for each map exposure. It is therefore not possible to remove sky noise the way we usually do for jiggle maps. Sky noise can be extremely severe and rapid on Mauna Kea. Under such circumstances sky noise variations can completely distort a scan map, especially for large chop throws. calcsky was originally developed to give us a technique for reducing sky noise in scan maps, but as we have seen it works also quite well for jiggle maps, as discussed in Section . calcsky computes a model of the source emission, and subtracts it from the data for all bolometers as a function of time. Several maps can be co-added.

Below we test calcsky on a small scan map, taken with a 20'' chop in RA. The map file, rn14_lon_dsp has already been baseline subtracted, pointing corrected, calibrated and despiked.

% calcsky
OUT_COORDS - Coordinate sys for sky determination /'RJ'/ > 
SURF: output coordinates are FK5 J2000.0
REF - Name of first data file to be processed 
/'rn14_lon_reb'/ > rn14_lon_rlb
SURF: run 14 was a MAP observation of RNO1b with RASTER sampling
SURF: file contains
SCULIB_PROCESS_BOLS: no data for exp 7 in int 1, meas 1
SCULIB_PROCESS_BOLS: no data for exp 7 in int 2, meas 1
SCULIB_PROCESS_BOLS: no data for exp 7 in int 3, meas 1
WEIGHT - Weight to be assigned to input dataset /1/ > 
SHIFT_DX - X shift to be applied to input dataset on output map 
(arcsec) /0/ > 
SHIFT_DY - Y shift to be applied to input dataset on output map 
(arcsec) /0/ > 
IN - Name of next input file to be processed /!/ > 
SURF Input data: (name, weight, dx, dy)
   -- 1: rn14_lon_rlb (1, 0, 0)

BOXSZ - Size of smoothing box (seconds) /2/ > 
MODEL - File containing source model /!/ >

We can now examine the sky variations with linplot. There seems to be clear systematic variations as a function of time, but the maximum deviation is only $\sim$ 150 mJy/beam, c.f. the jiggle map we did earlier (Fig. ), which showed sky noise variations of about 600 mJy/beam.

However, we can easily check how much we gain in noise performance if we remove the sky noise from the data. We therefore run remsky on the same data file that we processed with calcsky.

% remsky
IN - Name of input file containing demodulated map data 
/@rn14_lon_reb(280:340,90:300)/ > rn14_lon_rlb
SURF: run 14 was a MAP observation with RASTER sampling of object 
RNO1b
OUT - Name of output file /'rn14_lon_sky'/ > 
REMSKY: Using SKY extension to determine sky contribution

In this case the gain was rather marginal. The despiked data file gave an rms noise of 70 mJy/beam after running it through rebin while the sky corrected one improved by $\sim$ 0.5 mJy/beam (i.e. an improvement of less than one percent), when examined over the same area of the map, which means that it was not really worth doing. Nevertheless, I go through all six maps in the set, and find as I had expected the largest sky fluctuations for maps taken with a 65'' chop throw. In the last map of the set (65'' chop in Dec), the maximum sky fluctuations were $\sim$ 250 mJy/beam, or peak-to-peak sky noise variations of $\sim$ 500 mJy/beam, resulting in a 7% improvement in noise after subtracting the calculated sky noise variations.

calcsky does not work very well on a single map, but since calcsky can account for the chop throw, one can use a first version of the final map as a model for the individual sub-maps. If necessary, one can do a second iteration by using the sky corrected sub-maps to create a new improved map, which can be used as an even better model for calcsky.

From here onwards the rest of the reduction differs depending on the scan map mode.