Starlink Project
Starlink Cookbook 20.3

H. S. Thomas, Malcolm J. Currie, & H. A. L. Parsons

2021 June 22

Copyright © 2015-2021 Science and Technology Facilities Council,
& East Asian Observatory

The Heterodyne Data Reduction Cookbook




This cookbook provides a short introduction to Starlink facilities, especially Smurf—the Sub-Millimetre User Reduction Facility—and Kappa—the Kernel Application Package—for reducing, displaying, and analysing ACSIS data. In particular, this cookbook illustrates the various steps required to reduce the data; and gives an overview of the ORAC-DR pipeline which carries out many of these steps using a single command. Specialised pipeline recipes are discussed. The cookbook also introduces cube analysis with the Gaia display tool, and spectral analysis with Splat.


1 Introduction
 1.1 This cookbook
 1.2 Before you start: computing resources
 1.3 Before you start: Starlink software
 1.4 Options for reducing your data
2 Good to Know: Starlink
 2.1 File format
 2.2 Parameters
 2.3 How to find the current parameter values
  2.3.1 Extracting a value for scripting
 2.4 How can I view the metadata?
 2.5 What has already been done to the data?
 2.6 How to examine, process or extract a subset of your data
 2.7 How to get help
3 JCMT Heterodyne Observing
 3.1 Spectral-line observing
 3.2 Heterodyne instruments
  3.2.1 Nāmakanui frontend
  3.2.2 Ūū frontend (in commissioning)
  3.2.3 Āweoweo frontend (in commissioning)
  3.2.4 Alaihi frontend (in commissioning)
  3.2.5 HARP frontend
  3.2.6 RxA3 frontend (retired June 2018)
  3.2.7 ACSIS backend
 3.3 Observing modes
  3.3.1 On-source
  3.3.2 Reference
  3.3.3 Hybrid
4 Raw ACSIS Data
 4.1 Data format
 4.2 Visualising raw data
 4.3 Identifying and masking bad data
5 The ACSIS Pipeline
 5.1 Recipes and primitives
 5.2 The workflow
6 Running the Pipeline
 6.1 Checking and changing the science recipe
 6.2 Setting recipe parameters (optional)
  6.2.1 Setting recipe parameters by object name
  6.2.2 Setting recipe parameters by ORAC-DR internal header
 6.3 Setting quality-assurance parameters (optional)
 6.4 Specifying bad receptors (recommended)
 6.5 Starting the pipeline
 6.6 Pipeline output
  6.6.1 Why have multiple reduced files been generated?
7 Regridding your Data
 7.1 Running Makecube
  7.1.1 Reducing multiple observations
 7.2 Makecube options
  7.2.1 Specifying spectral bounds
  7.2.2 Masking receptors
  7.2.3 Including/excluding receptors
  7.2.4 Defining an output grid
  7.2.5 Data-regridding options
  7.2.6 Generating a variance map
  7.2.7 Sparse grid
  7.2.8 Creating a catalogue of receptor positions
  7.2.9 Tile dimensions
8 Analysing your Cube
 8.1 Smoothing your data
 8.2 Removing a baseline
  8.2.1 Advanced method
  8.2.2 What about findback?
 8.3 Collapsing your map
  8.3.1 Advanced method
  8.3.2 Moments maps
 8.4 Temperature scales
 8.5 Changing the pixel size
 8.6 Mosaicking cubes
 8.7 Cropping your map
  8.7.1 Supplying a template
 8.8 Filling holes in your map
 8.9 Checking the noise
  8.9.1 Visualising the noise
  8.9.2 HARP flatfielding
9 Advanced Analysis
 9.1 Changing co-ordinate frames
 9.2 Hybrid data
 9.3 Position-velocity diagram
 9.4 Creating channel maps
 9.5 Clump finding
 9.6 Using SPLAT to identify telluric emission
10 Using GAIA
 10.1 Removing a baseline with GAIA
 10.2 Creating channel maps with GAIA
 10.3 Contouring with GAIA
 10.4 Overlaying clumps and catalogues with GAIA
 10.5 Displaying average spectrum with GAIA
 10.6 Collapsing your cube with GAIA
 10.7 Three-dimensional visualisation with GAIA
 10.8 Sending spectra to SPLAT
11 Using SPLAT
 11.1 Opening a spectrum in SPLAT
 11.2 Display synopsis of spectrum
 11.3 Changing units of a spectrum in SPLAT
 11.4 Cropping a spectrum in SPLAT
 11.5 Rebinning a spectrum in SPLAT
 11.6 Estimating the noise in a spectrum using SPLAT
 11.7 Fitting a line in a spectrum using SPLAT
12 Getting your Data from CADC
 A.3 Running PICARD recipes
B Converting File Formats
 B.1 Converting a FITS file to NDF
 B.2 Converting an NDF file to FITS
C Scripting your Reduction
D Regridding versus Resampling
E Clump-finding Algorithms
F Viewing your Data with KAPPA
 F.1 Setting up xwindows
 F.2 Format axes
 F.3 Plotting a two-dimensional image
 F.4 Plotting spectra
 F.5 Plotting a grid of spectra
 F.6 Plotting two images side by side
 F.7 Selecting a different graphics device
G Classified Recipe Parameters
H Quality Assurance Parameters
I Removal of reference signal
 I.1 Remove the reference signal using ORAC-DR


ACSIS Auto-Correlation Spectrometer and Imaging System
ARD ASCII Region Definition
CADC Canadian Astronomy Data Centre
CSO Caltech Submillimetre Observatory
FITS Flexible Image Transport System
FWHM Full-Width at Half-Maximum
GAIA Graphical Astronomy and Image Analysis Tool
HDS Hierarchical Data System
JCMT James Clerk Maxwell Telescope
LSB Lower sideband
MSB Minimum Schedulable Block
NDF Extensible N-Dimensional Data Format
HARP Heterodyne Array Receiver Programme
QA Quality Assurance
RxA Receiver A
SAMP Simple Application Messaging Protocol
SDF Starlink Data File
S/N Signal-to-Noise ratio
USB Upper sideband
WCS World Co-ordinate System
WVM Water Vapour radioMeter


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