MIRIAD was designed for
Multichannel
Image
Reconstruction,
Image
Analysis
and
Display
MIRIAD can be run on the command line, and is easily built
into CSH, PYTHON, etc scripts for simple, or for quite
complex data reduction and image analysis procedures.
This afternoon's seminar will attempt to take participants on a
demonstration tour of MIRIAD's capabilities and documentation.
This may instructive, entertaining, or embarrassing,
Bring your laptops if you have, don't worry if not.
Melvyn
REFERENCES
Easy reading:
"A Retrospective View of Miriad",
Sault, R.J., Teuben, P.J., \& Wright,M.C.H., 1995,
in Astronomical Data Analysis Software and Systems IV,
ed. R. Shaw, H.E. Payne, \& J.J.E.Hayes, ASP Conf. Ser., 77, 433
http://astron.berkeley.edu/~wright/miriad/miriad_retrospective.ps
- a 4 page review of the HISTORY, GOALS, DESIGN, and IMPLEMENTATION
--------------------------
ON-LINE USER DOCUMENTATION
--------------------------
For ATCA
http://www.atnf.csiro.au/computing/software/miriad
For SMA
http://smadata.cfa.harvard.edu/miriadWWW
For ALMA
http://astron.berkeley.edu/~wright/miriad/miriad-audit.ps
http://astron.berkeley.edu/~wright/miriad/offline-audit-comparison.ps
These two memos were censured by the ALMA project, and eventually
published in watered down language as BIMA and IRAM memos.
These memos give a good description of the capabilites or not
in satisfying ALMA's requirements.
Documentation is built into the MIRIAD source code, and extracted
using the doc program.
e.g.
> doc telepar
Task: telepar
Responsible: Bob Sault
TELEPAR gives the characteristics of various observatories.
Its main use is to check that the characteristics are correct.
Keyword: telescop
Name of the observatory. Several can be given. If none are
given, TELEPAR simply lists the known observatories.
> telepar
Telepar: version 3.0 26-AUG-03
Known observatories are:
alma
atca
carma
ceduna30m
cso
gmrt
hatcreek
hobart26m
iram15m
jcmt
kittpeak
mopra
nobeyama45
nro10m
onsala
ovro
parkes
penticton
quabbin
sma
sza
sza10
sza6
vla
wsrt
-----------
DATA FORMAT
-----------
There are two types of data structure in Miriad.
1. UVDATA
The uvdata structure is used for single source, multiple source or
frequency, mosaiced, polarization, interferometer or single dish
observations. The data can be stored as real or complex floating
values, or scaled 16-bit integers. The metadata are stored as a
stream of named variables and values. Source names, frequencies,
pointing centers, are variables which can change throughout the
uvdata.
Miriad calibration tasks produce or use calibration tables and
parameters which are stored in the uvdata structure.
The history of observation and data reduction, including the steps and
parameters used in observing and reducing the data are stored in the
uvdata structure. Other structures, such as WVR data, and a copy of
the observing script and parameters, have been
easily added.
A "stream" of sampled data flows from the telescope.
The MIRIAD data format is well suited for on-line imaging.
2. IMAGES
Miriad images use a FITS-like format to describe the multidimensional
image data. The image is stored as floating point numbers. An image
contains a bit-mask for pixel blanking. The history of
observation and data reduction, including the steps and parameters used
in observing and reducing the data is stored in the image format. The
same format is used for single field maps, beams, multichannel, MFS,
mosaiced, polarization, and model images deconvolved using clean, maxen,
mfclean, mosmem, mossdi etc.}
----------------------------
SIMULATING UVDATA AND IMAGES.
----------------------------
MIRIAD has been extensively used to plan and simulate imaging
with CARMA, ALMA, and ATA. Several memos and downloadable demonstations
are available as BIMA, ATA, and SKA memos. Also available on
http://astron.berkeley.edu/~wright
E.g.
For CARMA
http://astron.berkeley.edu/~wright/sza_location.ps
http://astron.berkeley.edu/~wright/carma_memo27.ps
For ATA
http://astron.berkeley.edu/~wright/ata_imaging.pdf
http://astron.berkeley.edu/~wright/ata-32/test/mini_ml_256.html
For ALMA
http://astron.berkeley.edu/~wright/compact_configuration_evaluation_mosaicing.ps
http://astron.berkeley.edu/~wright/aca.ps
----------------------------------------------
LARGE-N ARRAYS: ATA, SKA and REAL TIME IMAGING
----------------------------------------------
The current radio astronomy paradym for data reduction is very time
consuming and unattractive for non-radio astronomers.
http://astron.berkeley.edu/~wright/ska_imaging.pdf
In this memo, we explore the imaging requirements and data processing
options for the large N SKA. We discuss imaging from the sampled cross
correlation function and direct imaging by beam formation. Cross
correlation of all antennas provides the most complete sampling of the
incident wavefront and allows imaging the full field of view of the
individual antennas. Extrapolation of existing and planned radio
astronomy correlators suggests that a 4000 antenna, 1 GHz bandwidth
correlator is feasible by 2020. Direct image formation requires
GHz data processing to phase the signals from all the antennas over
a 10^6 pixel image. The calibration must be made in close to real
time with the derived calibration parameters fed back into the real
time system for multiple phase centers. The large data rate and data
processing requirements suggests that the SKA should produce final,
calibrated images as its normal output.
http://astron.berkeley.edu/~wright/ska.ps
In this paper, we propose to integrate the imaging process with the
correlator hardware in order to handle the high data rates and imaging
problems for radio telescope arrays with large numbers of antennas and
large fields of view. We use FX correlators and beam formers with a high
data bandwidth into computer clusters to support a flexible programming
environment. The correlation function is computed with narrow frequency
channels and short integration times so that images can be formed over
a large field of view. Images can be made simultaneously in multiple
regions within the field of view by integrating the output from the
correlators at multiple phase centers on targets of interest,
calibration sources, and sources whose sidelobes will confuse the regions
of interest. Calibration is made in close to real time using a model of
the sky brightness distribution. The derived calibration parameters are
fed back into the imagers and beam formers. Images are made simultaneously
for multiple phase centers using an FFT algorithm in restricted fields
of view. Sidelobes from sources outside each of the regions imaged are
minimized by subtracting the model from the $uv$ data before imaging. The
regions imaged are used to update and improve the a-priori model, which
becomes the final calibrated image by the time the observations are complete.