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Goscha, D., Mehringer, D. M., Plante, R. L., & Sarma, A. 2003, in ASP Conf. Ser., Vol. 295 Astronomical Data Analysis Software and Systems XII, eds. H. E. Payne, R. I. Jedrzejewski, & R. N. Hook (San Francisco: ASP), 195

Calibration of BIMA Data in AIPS++

Daniel Goscha, David M. Mehringer, Raymond L. Plante
National Center for Supercomputing Applications

Anuj Sarma
University of Illinois

Abstract:

We summarize the general approach adopted for the calibration of millimeter interferometer data from the BIMA telescope using AIPS++ and illustrate the use of the relevant software tools. In particular, we will discuss flagging, phase calibration, flux calibration, and polarization calibration, and we will show how we take advantage of the unique capabilities of AIPS++ to meet the special needs of BIMA data. We will show how BIMA calibration tools can be used to hide some of the complexity of the processes while still allowing access to specialized variations if desired. We will illustrate how these tools are pipelined together for end-to-end processing both within the BIMA Image Pipeline and on the user's desktop. Finally, we will present a comparison of data calibrated in MIRIAD and AIPS++.

1. Introduction

We present the results of a comparison of calibrated millimeter data from the Berkeley-Illinois-Maryland Association (BIMA) Array using both AIPS++ and MIRIAD. In addition, we discuss the unique calibration capabilities of AIPS++ in calibrating BIMA data both on the user's desktop and in an end-to-end (e2e) pipeline. In particular we present:

2. Calibration of BIMA Data with AIPS++

AIPS++ allows for the concealment of some of the complexity of calibrating BIMA data through the use of custom tools. The bimacalibrater tool in AIPS++ is such a tool. bimacalibrater contains several functions needed in the calibration process, many of which are friendly wrappers around functions of the AIPS++ calibrater tool. These wrappers hide parameters not normally needed in the calibration of BIMA data and provide more suitable defaults for other parameters. The bimacalibrater functions hide much of the complexity of the calibration process while still allowing a high degree of customization for varied data.

One of the important aspects of the calibration process is the ability to view the antenna based gain solutions, flag bad data in the solution, and fit the solutions. Gain solutions are written to a gain table that can be accessed by the AIPS++ table tool, allowing for a high level of accessibility to the data. Once this has been done, an interactive user can use the plotcal function of the bimacalibrater tool to examine the gain table. If any bad data were noted after examining the gain table, a user could simply flag the bad data using the autoflag tool, or, interactively using the msplot tool (both part of AIPS++). In addition, it is also possible to flag and fit gain table solutions using the gainpolyfitter tool.

2.1 Calibration Process

The calibration process consists of three primary steps:
  1. Filling;
  2. Flagging/Editing;
  3. Calibration.
All of these steps can be carried out interactively on the user's desktop using the AIPS++ GUI, interactively using the Glish (the scripting language front end to AIPS++) command line interface, or in an automated fashion using custom Glish scripts. The BIMA Image Pipeline currently employs the bimacalibrater tool to do automated calibration of BIMA data.

3. Comparison of Data Calibration with AIPS++ and MIRIAD

In order to assess the robustness of calibration of BIMA data within AIPS++, several comparisons were made between data calibrated with and cleaned within AIPS++ and MIRIAD. Great care was taken at each step of the calibration process to ensure we were comparing ``apples to apples'' - data that were flagged in one data set were flagged in the other, the same clean algorithms were used in both cases, gain solution fits were both two point interpolations, etc. The following comparisons were carried out:

As a check on the flux density calibration, images of the calibrator 1733-130 were made. The specified flux density of this source during calibration was 2.8 Jy. The flux densities of the dirty maps were compared. The MIRIAD image produced a peak flux density of 2.66 Jy, while the AIPS++ data yielded a flux density of 2.81 Jy. In this particular case, AIPS++ did a better job in reproducing the correct flux density during calibration.

The next step in the calibration comparison was to examine the gain solutions produced in the two packages. There was no noteworthy difference in the gain solutions other than the fact that gain solution amplitudes in MIRIAD are the reciprocals of their AIPS++ counterparts.

A qualitative comparison of the images after a 1000 iteration clean (using the Clark clean algorithm) was then performed. The same data were calibrated and cleaned in AIPS++ and MIRIAD and then imaged. Figure 1 shows the results. In both cases, contour levels were chosen to highlight the background noise levels so qualitative comparisons between the calibrations could be seen more clearly. The contour levels are the same for both images.

Figure 1: SGRB2N calibrated and cleaned in MIRIAD and in AIPS++.
\begin{figure}
\plottwo{P3.11_1.eps}{P3.11_2.eps}
\end{figure}

Using the imstat command in MIRIAD and the image analysis tool in AIPS++, the RMS noise level for each cleaned image was measured. In both cases, the same off-source region was used. The results of this comparison are summarized in Table 1.


\begin{deluxetable}{lll}
\tablecaption{Image quality after calibration and clean...
...y/beam) & 5.50 & 5.25 \nl
Dynamic Range & 91 & 90 \nl
\enddata
\end{deluxetable}

Lastly the data were cleaned to a maximum residual cutoff of 0.115 Jy/beam and a similar comparison of background noise done. It should be noted that AIPS++ cleaned to that level faster than MIRIAD - 1437 iterations in AIPS++ and 4451 iterations in MIRIAD. The results are summarized in Table 2.


\begin{deluxetable}{lll}
\tablecaption{Image quality after calibration and clean...
...y/beam) & 5.5 & 5.4 \nl
Dynamic Range & 112 & 117 \nl
\enddata
\end{deluxetable}

4. Summary

Comparison of calibration of BIMA Array data in MIRIAD and in AIPS++ has been carried out. We found no significant difference in the gain solutions and images made from calibrated data from either package. Specifically we found the following:


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Next: Self-calibration for the SIRTF GOODS Legacy Project
Up: Calibration
Previous: Generalized Self-Calibration for Space VLBI Image Reconstruction
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