Flip frequency axis in UV data?

Hey, does anyone know an equivalent of 'TRANS' in the UV domain? I have two datasets that I need to DBCON, but they are in reverse order (i.e. CDELT > 0 for one, and CDELT < 0 for the other). Is there a task to flip the UV sideband?

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Amplitude Self-Calibration and Flux Levels

If you want to get really high dynamic range, self-calibration is a necessary. (See this post for the basics of self-calibrating in AIPS.) However, getting the fluxes right (especially for weak extended emission) is tough. See below for more details.



Usually you start off with a phase calibration boxing more and more sources as you improve the model. I usually start boxing fairly conservatively and then box more freely in later phase self-calibration interations. The amplitude self-calibration, however, is slightly different in that you need to include all the emission that's real and none that's not, i.e., you need to make the best possible image of your source. If you don't include all the emission (say the diffuse stuff), then you are essentially saying that there's zero flux in that part of the image and the fluxes in the resulting image will be forced downward closer to that value.

There are a couple things to check to see how well you're doing with the flux estimates. One is to plot the visibility amplitudes vs. uv distance. They should be more or less the same. If you have amplitudes in the amplitude self-calibration data that are significantly below that in the unself-calibrated data you've got problems. Another thing to check is how much total flux you're recovering. The total clean components you use (given in the output of IMAGR) should be close to the total flux in the image and the fluxes of sources shouldn't go down after amplitude self-calibration.

Thanks to Crystal Brogan and Bill Cotton for their help with this issue.

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Mulit-Resolution Clean Tutorial?

Here is a very reasonable request from an anonymous reader:

Can someone write about multi-resolution clean(MRC)..basically the steps involved and the way self cal should be carried out while using MRC ? thanks in advance.

which I can not fulfill, as I have never successfully pulled off a MRC. You might try looking at the EXPLAIN file for IMAGR in the section called "Extended Source Options".

If anyone knows better resources for learning about how to implement multi-resolution clean, please let us know!

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FreqID tolerance

And another question from reader Korinne:

With data all observed looking for a particular spectral line over a few objects, should you use different frequency ids for the objects or can you use the same frequency id? I've been using a suggested value of a 2 MHz frequency tolerance in the manual which means they all end up with the same frequency id, but I do not know if it's right.

Thoughts?

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Combining Data with Different Pointings

Here is a question from reader Korinne:

I have data from three VLA pointings in L band at the same frequency (with the same channel width) but with different pointing centres which cover an area of an object and partially overlap, unintentionally as the pointings don't overlap at the other frequency we observed that the observations were mostly set up for.

What is the best way to combine this data? I initially imaged and cleaned and then combined with flatn, but it seems like there might be some way to do it at the uv level, but from my readings of the cookbook I can't figure out how I would go about that, particularly in slightly more detail.

My answer below the fold...

I think you want DBCON, which can concatenate data in UV space. Try reading this post. The answer is basically that it depends on just how different your different data pointing are.

If you are actually talking about mosaicing a source, where your various pointings are offset by significant fractions of the primary beam, then FLATN might be your best bet. A bit more discussion of mosaicing here.

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Combining Spectral Line UV Data (with slightly different channel setups)

Update: See this lovely post by Adrienne

This is a very good question from reader SillyWolf. I know several of us have been wondering how to concatenate spectral line data sets that have approx. the same wavelength coverage, but different channel setups (different widths, different centers). So any advice would be greatly appreciated by many people!

There are two VLA archive data which were observed in spectral line mode.
These two archive data cover very similar frequency range.
The point is that:
not only they have different pointing center (about 1 arcmin away from each other), but also their reference frequencies do not match each other, so their frequencies don't match channel by channel !!
I want to combine these two data together. can I use DBCON to combine them in UV domain, or I have to resample one of these datacubes in image domain and combine them together?

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Averaging Image Channels

Here is a good question from Sanch:

I am trying to average spectral channels in an image cube. Can anyone suggest what's the best way
to do it in AIPS?
I don't want to go back to the UV data and re-reduce it to get the image, as the data I am
working on is huge (VLBI data).

And some more details:

She wants to average together channels in a channel map. In other words, she has a channel map with, say, 100 channels and she wants to turn it into a map with 50 channels. Channels 1 and 2 would be averaged together and become the new "Channel 1," channels 3 and 4 would be averaged together and become the new "Channel 2," and so on.

Thoughts, AIPS gurus?

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