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Help interpreting processed data (and their transforms)

  1. Feb 22, 2016 #1
    Hi,

    so I have the spatial distributions of detected hits in figure 1. When plotting fig 1 as a regular scatter plot I thought I could discern some sort of pattern. So I got the idea of taking its fourier transform and to see the result of the analysis. I am not very well acquainted with the program I am using (yet), so I am not completely confident in that I've done everything correctly, but bare with me.

    Questions:
    - How can the regular pattern in figure 5 be interpreted? The striations in the pattern resemble what I could see in the scatter plot. What about the zigzag pattern?
    - In figures 3 and 4, are the red areas equal to places in the spatial distribution where the gradient is the highest?
    - When trying to determine detector effects from data, where position and momentum of the particles are available, what could be worth investigating?

    Below are 6 figures in total. Every other figure matches. It's from the first and last detector in a series of 4.

    2 first figures: spatial distribution of hits
    2 middle figures: magnitude of the fourier transform (I called it spatial frequency, not too sure there).
    2 last figures: phase of fourier transform

    posBlinffcce.png
    Figure 1. First detector. Beam is rather collimated.

    posAlina313f.png
    Figure 2. Last detector. More divergence, naturally.

    fftmag07b83.png
    Figure 3. First detector. Magnitude of fourier transform.

    fftAmaga2c29.png
    Figure 4. Last detector. Magnitude of fourier transfom.

    fftfreq9949b.png
    Figure 5. First detector. Phase of fourier transform.

    fftAfreq667ac.png
    Figure 6. Last detector. Phase of fourier transform.


    Cheers,
    rjseen
     
  2. jcsd
  3. Feb 27, 2016 #2
    Thanks for the post! This is an automated courtesy bump. Sorry you aren't generating responses at the moment. Do you have any further information, come to any new conclusions or is it possible to reword the post?
     
  4. Mar 1, 2016 #3

    ChrisVer

    User Avatar
    Gold Member

    I don't think you can deduce something like that with these information?
    One thing you could do is make a new variable, let's say [itex]r[/itex] which shows you how spatially far away from the origin your events are [radius] :[itex]r= \sqrt{x^2+y^2}[/itex]. This variable (looking at the 1st plots) will get a high value for events close to r=0, (x=0,y=0) and drop as you move r>0.
    You can do the same for the [itex]F_x,F_y[/itex] into a variable [itex]f[/itex]?
    Then if you plot the f vs r I'm pretty sure you can deduce answers to this...
    If for example what you say is true, then the places were [itex]f[/itex] will be red (where the Fx and Fy were if you created f correctly) will also correspond to places where r was red (eg within a band around r=0)...
     
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