JCMT ACSIS Data

I’ve been trying to make presentable cubes for our observations of Wu Core done at JCMT with ACSIS. So far I’ve managed to produce nice, smooth cubes of data from HCN (3-2), HCO+(3-2) and C17O data (all the current dataset).

Please note that these steps are presented for an example and need not to be fully followed. Almost all of the parameters in each step are quite subjective and are done to satisfy my taste.

There is more extensive document located here: http://www.jach.hawaii.edu/JCMT/spectral_line/data_reduction/acsisdr/ 

Here is the sequence to produce the final cubes using STARLINK packages SMURF, KAPPA:

Step 1

> makecube in=raw_data autogrid=true pixsize=10 specbounds=\"60 130\" out=step1-initial-cube

Here we use autogrid and in principle pixsize is not necessary, but it’s there to be sure that things work as they should be. In our case pixsize=10 arcseconds since that’s the way we asked the positions to be separated from each other. Specbounds ensures that the final produced cube has velocity range of 60 to 130 since our line has 95km/s rest velocity.

Step 2

> mfittrend in=step1-initial-cube ranges=\"60 80 110 130\" order=3 axis=3 out=step2-baseline

Here we first create the baseline from the sides 60 to 80 and 110 to 130 km and  choose to fit 3rd order polynomial since there are some spectra in positions where baseline is not a straight line. 

Step 3

> sub step1-initial-cube step2-baseline step3-bs

In the next stage we subtract the produced baseline from the initial cube in order get baseline subtracted spectra in each position.

Step 4

> compave step3-bs compress=\[1,1,6\] out=step4-BinBy6

Here we bin the data by factor of 6 in order to smooth it. To further smooth the data one can maybe do some block or gaussmoth passes on the cube, but probably it’s not necessary at this stage.

Step 5

>  collapse in=step4-BinBy6 axis=3 estimator=mean out=step5-2d

Here we produce a 2D intensity map of the cube.

Step 6

> chanmap in=step4-BinBy6 axis=3 low=60 high=130 nchan=24 shape=6 estimator=mean out=step6-chanmap6by4

This stage is for producing a channel map of the data, like velocity-position map

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