So from here we want to do the clean algorithm. At first we can have a look

how the field looks like at the moment in the residual map. So for that we

use the command mappl. And then we have an image on the right side. At

first starting image of at least something. And you see there is a center

bright spot as I said which we assume. And now we need to start to model that

thing. We know that the most flux is coming from the center. So what we can do

is to tell divmap only search for components in a certain area. This is

done by so-called clean windows. Those clean windows can be defined in the

mappl command. So where we are right now you just click left into the field and

then you already see there is a rectangular that is being opened. With

that you can draw a window and click a second time and then you see that the

window stays where it is. And now for divmap this means when clean components

are being searched for only search within this clean window. Now we want to

keep it as this is for now. We close the window with a right click and get back

to our terminal. Now we want to search for 100 clean components. So type clean

100 and we define them to be 0.03. With 0.03 we set the clean

components to be with a flux scale of the factor of 0.03 for all of the

components. Then we confirm and then there is a bunch of information. So it

gives us like the info the first 50 flux components were cleaned with a total

flux of 474 milli Jansky and after a hundred components we have cleaned

roughly 600 milli Jansky. It gives also here the total information for total

flux that was subtracted with the components which is roughly 600

milli Jansky as we have seen above already. And then there is also more

statistic information of the residual minimum, the mean, maximum, the root mean

square and well of course again the model that we have built for now how

much flux is contained in the model in the latest model. We can have a look at

the components where they have been placed. We can just type in MAPL again.

Then we see already that the image looks slightly different because there is no

flux removed from the image and you see now the residual image. So the initial

image that we had at the beginning minus the clean components and to see where

those components were modeled you click in the field of the of the map and then

type M for models and there you see there is a bunch of pluses in there. So

let's zoom in with Z and there you see there is a few pluses in there. The gray

scaling in the background is now the residual so you need to imagine to add

so you need to add the flux of those components to the whole image and then

you get to the initial image that we had before. So this image will change in the

procedure of the imaging. Before we continue to clean we need to self

calibrate our data set to the models that we already have established and

that we do by the selfcal command and confirm and there we see how our root

mean squares change before and after the selfcal and our sigmas. Now we want to

add even more components and improve our model. But because typing all of

that stuff is kind of tedious and we are a bit lazy we can do loopings. So

DIVMAP is able to accept loop commands so you can for once you can add multiple

commands one after another with a semicolon. So what we did before is clean

100 0.3 and afterwards we did the selfcal so let's do semicolon selfcal

and afterwards we showed them the residual map to us so semicolon mapple.

Now it will perform three commands one after the other and we put it in just

one line so hit confirm it does all of the things and we see that it already

opened the new window. So let's have a look at the new window type M again and

now we see something interesting. So there is not only green pluses in there