PNe statistics

This is a continuation in the comparison with PNe data, this time from Feldmeier. Our aim on this page is to see if there is any correlation between the location of PNe and of ICL (i.e. the unmasked pixels in our images). To do this we generate the following graphs with this method:
1) select a square region of size r with a location determined by the method being used
2) count the number of PNe in that region by using the Feldmeier catalog data file and get them into density: N / arcmin^2
3) average the intensity on the pixels in that region and convert to L_sun / arcmin^2

The other two important parameters that can be varied are:
- which image of ours we used: masked or unmasked
- how the selection of regions is done

We will use BOTH the masked image and the unmasked to see differences between them.
For selection, we will do one run with completely random selection (unbiased - 1,000 random selections are made) and one run selecting regions that each have a PN at their center (biased).

Given below are the resulting graphs for 3.fits and 7.fits. We omitted 4.fits to save time and do not expect that it would be very different anyway.
For both fields, the range (r) is varied through values of 10, 25 and 50. This range is not actually the "size" of the box, but the size of the square box is 2*range + 1.
For each value the method was performed on the masked and unmasked image with biased and unbiased selection.

notes:
> The data point appearing at (0,0) in every graph is an artifact of SM's fits reading that should have been manually removed. It was not noticed until after the (time-consuming) procedures had been completed.
> In the masked samples, there may be more than one data point co-existing at L_sun/arcmin^2 = 0 as some random regions do not actually have ANY (unmasked) pixels in them.

Code: /Virgo/FeldPNe/PNe_run.sm PNe_run <-- all night running version to get a lot of data processed
          /Virgo/FeldPNe/PNe_run.sm r <-- reconstructive program to take the data outputs of PNe_run and generate the graphs
3.fits

    

     


     

    


    

     


7.fits



    

    


     

     


[this one was taking too long and wouldn't have shown much different/insightful]    

[this one was taking too long and wouldn't have shown much different/insightful]     


OK so those aren't very conclusive, let's try another approach that selects regions a little differently


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