Statistics

After the finished mask from this process was generated, we can start to do some statistics on the masked images.

We are using newspikemode to do the statistical analysis. It has these input parameters

 
-in input file name [infile.fits]
-global find mode for whole image [false]
-bin bin size in pixels [64]
-regions estimate mode in regions [false]
-mask use bad pixel mask [false]
-bigfile big mode image [bigfile.fits]
-smallfile small mode image [smallfile.fits]
-ismooth histogram smoothing [13]
-percent downhill fraction to sum to mode [0.9]
-bhist histogram bin size [1]
-llim lower value [-100]


For our purposes, we are most concerned with the ones in green.

This program finds the mode of an image in certain sized bins across an image and displays the resulting data as another image with pixel values equal to the calculated mode.

Example:
Core.fits with final mask (red)


result from newspikemode:

bin = 32, bhist = 0.1


These results are, initially, messy/troublesome. Newspikemode is having trouble with our data as it doesn't have the spike that the program is trying to get rid of. This results in problem. Our data are not integer-ised either, another thing the program was built for. To start, we are going to find the median instead of the mode, but still for bins across the image as well as for the entire image.

For Core.fits (with mask of CoreRSMCu26.pl, as before), the median histogram image is:

This image was displayed by opening it in ds9 and setting the scale between -5 and 20, contrast of 6, bias of 0.3.
It also has -bin = 64 and -bhist = 1

Decreasing the -bin size is good to a point. The next images show it being decreased:
-bin = 36
 -bin = 25
 -bin = 16
Clearly when the -bin size reaches 16 the masked stars start popping up everywhere and it is not very useful.

Here are the other fields as well



Using medians instead of modes we generate the following data table:
Field
 u = 25
 u = 26
 u = 27 
fraction masked
 global median
 fraction masked
 global median
 fraction masked
 global median
Core
  0.1416527
 3.275757
 0.1681903
 3.096069
 0.3198190
 2.068115
Sub
 0.3121588
 11.44214
 0.5129969
 8.312256
 0.7818369
 4.194458
LPC
 0.4069117
 2.552124
 0.4139943
 2.476685
 0.5046802
 1.523071
FCJ
 0.1474154
 13.19031
 0.4039163
 10.12512
 0.8240633
 5.402344
1
 0.1861363
 6.453247
 0.2097798 6.305786 0.5089872
 4.443604
3
 0.2012744
 12.97534
 0.4417390
 9.933655
 0.8233280
 5.205078
4
 0.1844936
 3.831909
 0.1961103 3.768799 0.3185990
 3.112549
7
 0.1598105
 5.417236
 0.1988923 5.190918 0.4168574
 3.930054
VMF
 0.4263659
 5.172729
 0.4783481
 4.517578
 0.6285754
 2.649048
    ^^ VMF is the whole image and statistics are only included "for fun"


Now we begin to compare to Feldmeier and Aguerri results.



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