Chapter 5
The ACSIS Pipeline

The Orac-dr pipeline [4] is a generic automated data reduction pipeline that can process your raw JCMT data and return advanced data products: baselined single observation cubes, mosaicked and co-added cubes, moments map and clump catalogues. [15]

It has advanced algorithms for the common routines such as baseline subtraction. The data processing is performed using standard Kappa, Smurf, and Cupid applications, the main ones of which are described in later chapters.

5.1 Recipes and primitives

Reduction recipes in Orac-dr consist of a series of stand-alone processes, known as primitives. These primitives are linked together to form data reduction recipes. Each primitive can be fed different input data depending on the nature of the recipe in question and may sometimes be omitted altogether.

There are four main science recipes available, each tailored to different type of observation:

• REDUCE_SCIENCE_NARROWLINE
  
• REDUCE_SCIENCE_BROADLINE
  
• REDUCE_SCIENCE_GRADIENT
  
• REDUCE_SCIENCE_LINEFOREST

A summary of these recipes is given below.

There are also variants of the recipes with the following suffixes.

5.2 The workflow

You can follow this commentary via the flow chart in Figure 5.1.

Two notations are used in the following list:
∙ a_cube to mean the regridded cube of a single observation.
∙ g_cube to mean the group cube which is a co-add of all the a_cube files.

Note that the pipeline will co-add data into a group file whenever it encounters observations with identical LO frequencies, base positions and bandwidths. You can also force a set of observations, such as ones of the same object taken on different nights, to be regarded as a single group to be combined via the oracdr -onegroup command-line option.

(1)

The raw data are copied to the local directory. Typically, subsystems are treated as individual and separate observations by Orac-dr except for hybrid-mode observations.

(2)

Because data acquisition is asynchronous, time slices are not necessarily written in sequential order. The next step sort the time-series data into time order. This makes it easier to search for intermittent bad data.

(3)

The noisy ends of the spectral band dwarf most astronomical signal, and hence are removed as follows. The spectra are collapsed along the receptor using the ‘sigma’ estimator to form a single spectrum. A constant value background is then fit to the resulting spectrum. The fitting regions are used to determine where the spectrum gets noisier (i.e. higher RMS values in the RMS spectrum). These high-noise regions are then trimmed from the ends in the frequency axis. There is also an alternative to trim specified percentages through the TRIM_ recipe parameters (see Table G.2).

(4)

The DC-level offset between corresponding sub-band observations is determined using the median of all the spectra. The DC offset is then subtracted from the sub-band spectra, and the resulting sub-band spectra are mosaicked together to form single time-series cube.

(5)

Regions or individual spectra containing high- and low-frequency interference from local sources are identified and flagged. Bad detectors are identified by comparing the deviation from linearity of each detector’s baseline.

(6)

Strong spikes ($\pm$150) are replaced by bad values.

(7)

Quality assurance checks are run on the raw cubes. See Appendix H for a description of the checks performed. Any time-slices failing any of these checks gets flagged as bad and are not included in the group co-add that follows.

(8)

Once all the raw observations have undergone the initial processing, the individual time-series files are combined to form a g_cube.

(9)

The g_cube is smoothed by an amount specified by the particular science recipe being used. See Section 5.1 for details on the different recipes.

(10)

mfittrend is run to find the baseline regions on the smoothed g_cube. The ranges are determined automatically by setting auto=true. A baseline mask is written out.

(11)

The baseline mask is then applied to the unsmoothed g_cube and mfittrend is re-run. This time however, the emission regions have been masked out, so all remaining data are included in the fit by setting auto=false. The resulting baseline is subtracted.

(12)

Moments maps and noise maps are made from the baseline-subtracted g_cube.

(13)

If another iteration is required, continue to Step (14). If not, the processing is complete.

(14)

The baseline mask is converted back to the time series with unmakecube. There it is applied to the time-series data for each observation.

(15)

A baseline is fit to the masked time-series data using mfittrend and the result is subtracted from the unmasked time-series data. This can be a higher-order polynomial (up to 15${}^{\text{th}}$ order), although a linear fit is often used.

(16)

A flat field, i.e. the relative responsivities of the detectors, is optionally created and applied. It involes making cubes for each each receptor combining all observations on a given date to improve the accuracy. There is a choice of analysis methods, but an iterative approach of thresholding—to exclude the noisy baseline that would dilute the comparisons—worked best on most suitable observations. Those with low signal-to-noise or signal only originating in a small percentage of the area mapped are not amenable to determining the receptor-to-receptor responses. (See Table G.5).

(17)

An a_cube is made from the baseline subtracted data for each observation.

(18)

The individual baseline-subtracted time series are combined to make a new g_cube.

(19)

Return to Step (9).