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Volume 2 - Generic Processing
AIPS++ Group, Editor: Neil Killeen
A postscript version of this note is available.
Contents
Synthesis Calibration
Introduction
Calibration philosophy
The Measurement Equation
Data representation and
the calibration mechanism
What are the current calibration
capabilities in
AIPS++
?
Practical use of the calibrater tool
Setting up the calibrater tool
Initial calibrator models
Uv-data selection
Calibration table conventions
Solving for visibility-plane effects
Establishing the flux density scale
Correcting the observed data
Extending calibration to bandpass, polarization, etc.
Self-calibration
References
Imaging, Deconvolution and Self-calibration
Overview
Required Tools
Data Requirements
Setting up Imager
Construction
Selecting Data
Setting the Image Parameters
Channel Selection and Combination
Weighting
Creating and Deconvolving Images
Creating Images
Deconvolution
Specifying the deconvolution region
Wizard interface to Imager
Component Models and Primary Beams
Multi-scale CLEAN
General
The
imagermultiscale()
function
Wide-field imaging and Mosaicing
Combining single dish and interferometer images
Self-calibration
Summary of Imager functions
Setting/seeing the basic state
uv selection and filtering
Masking
Create images
Deconvolution
Image Combination
Utility
Calibration and self-calibration
Examples of Imaging Scripts
Multi-frequency synthesis image of one field
Multiple regions on the sky simultaneously
Spectral-line imaging
Using Clean and MEM
Deconvolver and multi-scale CLEAN example
Imaging and Self-Calibrating
Wide field imaging
Background
Possible solution to the wide-field imaging problem
Available Tools
Basic capabilities
Faceting size
Further Advice
Deconvolution
Self-calibration
Masking
Outlier fields
Models
A worked example: VLA 4m imaging of Coma
Special cases
Troubleshooting
Insufficient faceting
Non-isoplanatism
Asymmetric primary beams
Clean diverging on the edges of the facets
Bibliography
Mosaicing (Multi-field imaging)
Mosaicing Background
The
AIPS++
Mosaicing Solution
Advantages of Incremental Deconvolution with an Approximate PSF
Mosaicwizard for Quick, Simple Mosaicing
Fundamental and Necessary Details
Set the Data Fields
Set the Image
Setting the Voltage Pattern (primary beam)
Weighting
Deconvolving
Advanced Details
Controlling the Major Cycles
Details with Multi-Scale CLEAN
The
imagermultiscale()
Function
Details with MEM
Using Convolutions Instead of Visibility Subtraction
Outlier Fields
Component Models
Flux Scale Images
Masks
Self-calibration
An Example Mosaicing Script
Bibliography
GBT Continuum Single dish Imaging
Introduction
Filling single dish data into AIPS++
GBT
Loading the continuum calibration utilities
Data examination and inspection
Basic gain calibration
gc.setdata(msname)
gc.contcal(tcal=1.0,average=T,baseline=F,nfit=0,range=20)
Imaging
gc.makeimage(imname='scanimage',gridfn='SF',receptor=1)
gc.covercorr(imname=F,wname=F)
gc.plotsource(imname,nsource=1)
Example
Single Dish Analysis
Introduction
Getting Data Into Dish
MeasurementSets
Facility filler (e.g., GBT)
SDFITS data file (e.g., Arecibo Observatory)
UniPOPS SDD data file
Dish demo data
The Dish Graphical User Interface
Results Manager
Working sets, SDRECORDs, and everything else
Menubar and Message Line
Browsing
Inspecting
Operations
Averaging
Baselines
Calculator
Function on Data
G Fit
Re-gridding
Saving
Selection
Smoothing
Statistics
Write to File
Saving/Restoring State
The Dish Plotter
The Dish Command Line Interface
Recipes
Recipe 1: Change syntax to suit
Recipe 3: Add a function to DISH (or fun with extensibility)
sditerator
SDFITS
EXTNAME keyword
Virtual columns
The DATA column and the DATA axes
CORE keywords and columns
SHARED keywords and columns
Other columns
Multiple SDFITS tables in a single file
Copyright
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