Removing Stripes from Radio Images.

Aritra Basu.NCRA-TIFR, Pune University Campus, Ganeshkhind Road, Pune - 411 007

aritra@ncra.tifr.res.in

Bad baselines in the UV-data cause stripes in the image plane which are difficult to identify. However, the spacingof the stripes gives us a rough idea of the location of bad baselines in the UV-data. An example of such an unwantedfeature in the map is shown in Figure 1.

Figure 2 shows a cut along the indicated yellow line of the intensity. It clearly shows a periodic behaviour. Roughlythe stripe angular spacing is ∼200′′. This translates to a UV-distance of ∼ λ×206265/200. In this example the mapis at 325 MHz, which is about 0.9 m. This corresponds to a UV-distance of 0.93 kiloλ. Thus, there is a bad baselinewhich is roughly of length 0.93 kiloλ.

One can now find out the actual baseline responsible for this very stripe precisely. Just having the knowledge of thelength of the bad baseline won’t suffice as there are several baselines of similar length. Depending on the positionalangle and distance from the image phase center of the stripe, one can find out, the bad baselines. Figure 3(a) showsthe UV-data in the estimated range. There is no bad baseline that sticks out. Also, while imaging extended sources,as in this case, one cannot afford to throw away baselines of such a short length without reason.

Figure 1: Unwanted stripes in the map.

To find out the baseline responsible for the stripe, we need to see how the UV-data, sampled at those UV-pointswhere there are actual data taken, would have looked like given the stripes. It’s like simulating the UV-data from animage which is composed of just the stripe. To do so, we need to make a UV-data file which is just noise, similar tothe noise in the map, or a bit less, so that even the faint stripes also stick out. The task uvmod in AIPS can makesuch a file. We take the file from which the image was made and set the parameters as:> task ‘UVMOD’> getn nuv # The UV data file> ctype 0; fmax 0;fpos 0; fwidth 0; zerosp 0 # No source is added> factor 0 # Multiplying the UV data with zero> flux 70e-6 # Adding noise of 70µJy.The file created will have data only at those points which are present in the actual UV data.

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Figure 2: The cut along the stripe to show the stripe spacing in image plane. In this case the stripes are separatedby about 200′′.

Now one has to take a sub-image from the full image, of the part where the stripe is present, preferably from asource free region. Such a sub-image is shown in the right side of Figure 1. The task subim does the cutout. Theparameters to be given are:> task ‘SUBIM’> default # To clear any previously retained inputs.> getn nimage # The image file where stripes are present.> tvwindow # Select the relevant part of the image containing only stripes.> go

We have to generate the UV-data using this stripe. In doing so one needs to take a Fourier transform of the image.Thus the number of pixels on both the axes in the image should be of the order 2n. The sub-image may not havesuch number of pixels. To make the sub-image of size 2n

× 2n pixels, we need to pad the image with zeros. The taskpadim does the padding. The inputs are as follows:> task ‘PADIM’> getn nsubim # The sub-image to be padded.> imsize 2n 2m # The nearest power of 2 from the size of sub-image.> cparm 0> go

We just need to take the Fourier transform of this padded image to find out the bad baseline. The task uvsub helpsus to do this. The input are as follows:> task ‘UVSUB’> default> getn nuvnoise # The generated noise file.> get2name npadim # The padded image whose Fourier transform is to be taken.> nmaps 1> cmethod ‘DFT’; cmodel ‘IMAG’; factor -1 # Adding the image to the noise without any CC.> opcode ‘MODL’> go

Having done this, the generated model UV-file looks like Figure 3(b). It shows the UV-plot of the model generatedfile. Clearly, bad baselines at the expected UV distance is seen. Using the task uvfnd one can find out the baselines.In this case the baselines 8 − 13 and 1 − 10 were found to be bad. Figure 4 shows the amplitude as a function oftime of the real data.

Figure 5 shows the image after flagging the bad baselines from the data. The stripes that were seen in Figure 1 areclearly gone. However, the next level of stripes come up in the contrast now. One needs to carry out the entireexercise for the next level of stripe to get a better and better image.

Plot file version 1 created 26-FEB-2010 10:17:30Amplitude vs UV dist for NGC5055.UVAVG.1 Source:NGC5055Ants * - * Stokes RR IF# 1 Chan# 10

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Plot file version 1 created 26-FEB-2010 12:59:15Amplitude vs UV dist for STRIPE.UVSUB.1 Source:NGC5055Ants * - * Stokes RR IF# 1 Chan# 10

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Figure 3: The UV plots in the estimated UV range of 0 − 2 kiloλ. (a) The UV plot of the actual data. There is noobvious bad data that are seen and can be responsible for the stripe. (b) The UV plot of the model generated data.Clearly, one can see a few bad baselines sticking out at the estimated UV distance.

IF 1 CHAN 15 STK RRAmplitude vs Time for NGC5055.UVAVG.1PLot file version 2 created 26-FEB-2010 14:42:26

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IF 1 CHAN 15 STK RRAmplitude vs Time for NGC5055.UVAVG.1PLot file version 3 created 26-FEB-2010 14:45:08

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Figure 4: The identified bad baselines in the actual UV data. (a) The dead baseline 8 − 13, (b) A bad time rangefrom about 01:03:40:00 to 01:04:20:00 of the baseline 1 − 10.

Figure 5: The image after removing two bad baselines causing stripes.