MIRIAD was designed for


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.



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.