Feature Request: Add Central Beam to Model Peak Intensity Comparison Output to Gaussfit_Catalog

[jmangum]

To better identify poor gaussian fits from gaussfit_catalog, add comparison between central beam peak intensity and model-derived peak intensity to output.

[keflavich]

New version has this:

In [2]: tbl['ampguess', 'peak', 'amplitude']
Out[2]:
<Table length=10>
    ampguess          peak         amplitude
    float64         float64         float64
--------------- --------------- ---------------
   2.1532895565   4.35684013367   2.36861851215
 0.208429336548  0.472078025341  0.187586402893
  1.99721932411   3.98094463348   2.18141410208
  1.10862857103   2.46066641808   0.99776571393
0.0367745868862 0.0618809685111 0.0330971281976
 0.666082799435  0.994039654732   0.71930501079
  1.80831575394   3.64649200439   1.98914732933
 0.514871001244   1.06097972393  0.463383901119
            nan             nan             nan
  3.05733430386   5.95134210587   3.16472439548

'ampguess' is the peak intensity in the map minus the background. Is this what you need?

[jmangum]

Thanks Adam. I am not sure if this is what you were referring to back in October when we discussed how to identify bad gaussian fits. Here is the relevant part of the email thread on this subject:

Hi Jeff,

There really is no substitute for by-eye checking unless you want to spend time developing heuristic tests to quantify it.

In that image, the top left is the original data, top right is the model fit, bottom left is the residual, and bottom right is the original image again. The white circles are the centroid and (I think...) 1,2,3 sigma contours of the model. The red ellipse is the beam positioned at exactly the source center.

One heuristic test I can suggest, based on this image, is to compare the peak intensity within the central beam to the peak intensity of the model. If they are far off, it means the model has identified a different peak.

On Wed, Oct 25, 2017 at 5:47 PM, Jeff Mangum jmangum@nrao.edu wrote:

Hi Adam,

I am working through the results from gaussfit_catalog on our NGC253 and NGC4945 ALMA data and doing LTE column density calculations on those derived integrated intensities. Baring actually looking at each fit, I was wondering if there is a good way to identify poor fits? As as example of how just looking at the output .ipac file can lead one astray, attached is a fit to the H2CO 3(03)-2(02) integrated intensity image (from CubeLineMoment) and the resultant gaussfit_catalog output png for one of the sources in the catalog (source number 6). Looking at the .ipac file, other than the fact that the deconvolution failed (which it often does), and maybe the uncertainty on the fit to the centroid and FWHM are a bit on the large side, I don't see any serious red flags for the fit to source number 6. Looking at the png file, though, suggests that this fit is just plain bogus and should be thrown out as there is no peak for this source in this line. Is there something in the .ipac file that would have tipped me off to this bad fit?

[keflavich]

This is an example of a good fit:

This is source 6, a questionable-to-bad fit:

And here's the table:

|Name|      amplitude|     center_x|      center_y|   fwhm_major|    fwhm_minor|           pa|deconv_fwhm_major|deconv_fwhm_minor|deconv_pa|         chi2|         chi2_n|    e_amplitude|    e_center_x|    e_center_y|  e_fwhm_major|   e_fwhm_minor|           e_pa|       ampguess|           peak|success|
|char|         double|       double|        double|       double|        double|       double|           double|           double|   double|       double|         double|         double|        double|        double|        double|         double|         double|         double|         double|   char|
|    |               |          deg|           deg|       arcsec|        arcsec|          deg|                 |                 |         |             |               |               |           deg|           deg|        arcsec|         arcsec|            deg|               |               |       |
|null|           null|         null|          null|         null|          null|         null|             null|             null|     null|         null|           null|           null|          null|          null|          null|           null|           null|           null|           null|   null|
    1 0.0330971281976 11.8876535628 -25.2906894676 1.55463655872 0.969706850759  90.873796339               nan               nan       nan 4.79905500803 0.0151389747887 0.0128356433253  48.6772034601  85.3686348873  25.0350717427   11.6029546133   18.5427479072 0.0367745868862 0.0618809685111    True 
    2  0.187586402893 11.8845808051 -25.2886822473 1.41931315381 0.739570465643 153.825261167               nan               nan       nan 167.147362639  0.528947350124 0.0884778952978   41.817737274  28.2966091056  11.8282734925   5.40346837008   7.71721248973  0.208429336548  0.472078025341    True 
    3             nan 11.8830655703 -25.2913971382 1.51676917076 0.871051311493  82.154258728               nan               nan       nan           0.0             0.0             0.0            0.0            0.0            0.0             0.0             0.0             nan             nan   False 
    4   2.18141410208 11.8867727741 -25.2892229739 1.32984523682 0.685733112691 163.383251059               nan               nan       nan 1068.98125104   3.43723874933  0.238630079435 0.606727905731 0.347243722092 0.163079245224  0.073649784648    0.1103204833   1.99721932411   3.98094463348    True 
    5   2.36861851215  11.887383827 -25.2888052687 1.22941980177 0.736365378632  165.96163434               nan               nan       nan 1163.57937468   3.69390277675  0.247884593802 0.518989848134 0.327784712474  0.13341493468 0.0734636030933   0.13424605502    2.1532895565   4.35684013367    True 
    6   0.99776571393 11.8882214716 -25.2880535066 1.38008029732  0.65328848362 147.436840736               nan               nan       nan 1658.91379361   5.23316654136  0.331369593078  68.7417755404  49.3293709719  20.5264305465   7.03302070636    9.0050297043   1.10862857103   2.46066641808    True 
    7   3.16472439548 11.8888129301 -25.2877294591 1.28099562938  0.65328848362  174.79273498               nan               nan       nan 10207.0030293   32.1987477264   22196101.1986  5948133.94914   565170.38285  8814786.47171   6970.14151037    52977.896397   3.05733430386   5.95134210587    True 
    8   1.98914732933 11.8901868618 -25.2869735305 1.22993147125 0.749323884691 167.798818787               nan               nan       nan 628.464484344   1.99512534712  0.180656570325  0.45578977783  0.28701698683 0.116974410241 0.0651900883286  0.121538734599   1.80831575394   3.64649200439    True 
    9   0.71930501079 11.8914471746 -25.2864124987 1.12072516078  0.65328848362 165.298965782               nan               nan       nan 123.497445356  0.397097895035  0.125986289619 0.808753533942 0.314707495538 0.203430721324 0.0567815465898 0.0788992689132  0.666082799435  0.994039654732    True 
   10  0.463383901119  11.892284475 -25.2866275582 1.32758941291 0.672688199193 143.586419521               nan               nan       nan 898.701680423   2.83502107389  0.223141697036  67.6208185512  58.4659221356  20.5635046564   9.48179709038   14.3521478867  0.514871001244   1.06097972393    True 

So I guess the answer is 'no', there's nothing obvious in place. The chi^2 is a bit of a hint that the fit wasn't great, but sources 4 and 5 have similar chi^2 and have excellent fits.

[jmangum]

Wanted to pump some life into this feature request for gaussfit_catalog. Extracting an idea from @keflavich above regarding how to distinguish between a good and bad gaussian fit when only looking at the .ipac file output:

"One heuristic test I can suggest, based on this image, is to compare the peak intensity within the central beam to the peak intensity of the model. If they are far off, it means the model has identified a different peak."