Faraday rotation measure synthesis in AIPS. The new task FARS

L. Kogan, F. OwenNational Radio AstronomyObservatory

Socorro, NM USA

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RMTF functions for 8 frequencies in (1-2) GHzThe first row: (Re, Im) for the original set of

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The task carries out:

1. Picks up the U polarization data from the first input image as real and Q polarization data from the second input image, as imaginary of the complex input data2. Fourier transform of the complex input data along the lambda square axis3. Clean out the given number of complex components using the pre -calculated RMTF (as DB)4. The output images (cubes) can be either the full Fourier transform along axis for each image pixel (CLEANed or not CLEANed), or the 3 parameters of the only complex component. Only one output (CUBE) is used at the last case.5. The cleaned components can be convolved with real part of the RMTF(DB), or with the Gaussian of the given FWHM.6. The residual (Re, Im, or Amp) can be sent to the output also.7. The RMTF (DB) function (Re, Im, or Amp) can be sent to the output also under control of input parameters

The AIPS task FARS

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One-D Clean of rotation measure spectrum and following convolution with Gaussian of FWHM=20 1/m^2; Only 8 frequencies were used

The first row: 2 real components: 1. ampl. =0.2 , RM=-80; 2. ampl.= 1.0 , RM =100The second row: 2 complex components:

1. 0.2*expj ( /4)=(0.14+j0.14), RM=-80; 2. 1.0*expj ( /3)=(0.5+j0.86), RM =100 The left column is RE; the right column is IM

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One-DClean of rotation measure spectrum and following convolution with Gaussian of FWHM=20 1/m^2; 101 frequencies are used

2 complex components:1. 0.2*expj(pi/4)=(0.14+j0.14), RM=-80; 2. 1.0*expj(pi/3)=(0.5+j0.86), RM =100 1. 0.2*expj(pi/4)=(0.14+j0.14), RM=-80; 2. 1.0*expj(pi/3)=(0.5+j0.86), RM =100

The left column is RE; the right column is IM

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CONCLUSIONS

1.The AIPS’ task FARS is ready to serve the users 1.The AIPS’ task FARS is ready to serve the users interesting in Faraday rotation synthesisinteresting in Faraday rotation synthesis

2. The especially good results are expected with EVLA 2. The especially good results are expected with EVLA data, where the better frequency axis filling is expected data, where the better frequency axis filling is expected