Open d-l-walker opened 11 months ago
The Brick (field ao
, #41) has much more significant negative bowls in the joint 12m/7m + TP feather cube (left) vs. the feathered-only (right).
For field i
(#138), the joint 12m/7m + TP feather image (right) appears to have more larger scale emission, but also more significant negatives.
In the same field, when averaging over an area comparable to the single dish resolution, the fluxes are similar between the two approaches, possibly hinting at the issue being with the joint 12m/7m imaging.
Ash testing the following with MOPRA SD data: 1) including the MOPRA data to the cleaned 7m+12m images and it isn’t great, there are still a number of strong negative bowls in the image (hasn’t tested if its better than the TP);
2) increasing the weighting factor of the single dish in feather (attached show MOPRA+7m+12m; factors of 1, 2, 3, 4, and 5). You can see that upping the factor does solve the bowls, but some other artefacts are present (and the flux gets too high).
It was suggested in the WP1 meeting that this should also be tested on the feathered 12m + 7m data, rather that the jointly imaged data.
A quick comparison between the jointly imaged 12m/7m data for field i
(left) vs. the residual (right) is shown below for a single channel. This shows that the bulk of the brighter, more compact emission is being cleaned and captured in the mask, but the larger scale emission is not being cleaned well enough. This could be leading to the poor images that we're currently seeing.
As noted in the parent comment, we will try cleaning deeper and relaxing the masking to include this larger scale emission. This will increase the already lengthly cleaning times, so initial tests will be done on single channels/sub cubes.
Averaged spectra of the HNCO image and residual for the field shown above.
Here is my cleaning routine I used for Cloud D+E/F 1mm ALMA data. I wrote this a while ago so apologies its a bit of a mess, but all the functionality we discussed should be there, and modules in the directory...
https://github.com/ashleythomasbarnes/interferometry_analysis/blob/master/casa/cleaning_module.py
cyclefactor
, but this takes a long time.
cube | min | max | std | sum | mean |
---|---|---|---|---|---|
Sgr_A_st_t.TP_7M_12M_feather_all.hnco43.image.statcont.contsub.fits | -0.255 | 0.278 | 0.019 | 715997 | 0.0024 |
Sgr_A_st_t.hnco43.deep_clean.relaxed_mask.tp_startmodel.image.pbcor.feather.fits | -0.176 | 0.307 | 0.017 | 751702 | 0.0027 |
We probably need to try to quantify 'looks better'. I think there was a proposed statistic in the Plunkett paper (https://ui.adsabs.harvard.edu/abs/2023PASP..135c4501P/abstract), but I'm not sure that's valuable.
I'd like to know what the effective beam size is. That's some combination of 'what does the header say' and what is there in reality...
Also it'd be interesting to show the difference image (feathered - jointly cleaned)
Sure, I agree. This was just a quick look at some comparisons. I haven't had a chance to look into properly quantifying this yet.
Here's the difference moments (joint - feathered)
And feathered-joint since that's what you asked for ;)
This issue is to track all relevant discussion related to our approach to obtaining fully combined image cubes. The aim here is to do some tests to get the imaging in the best possible shape, and then determine whether we really need to do the joint imaging, or whether the feather-only approach is good enough.
@d-l-walker will work on some tests to re-do the joint imaging of single channels and sub-cubes with refined masking and deeper cleans to see whether this improves the results.
We should also look into using the TP data as a startmodel for tclean to see whether this improves the results. @d-l-walker will try to look into this too, but feel free to investigate this if you have the time/inclination.
@ashleythomasbarnes has also done some initial tests and comparisons, which will be summarised in a following comment.