Physics307L:People/Mondragon/Notebook/071003: Difference between revisions

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==Poisson distribution==
[[Physics307L:People/Mondragon/Notebook/071003 | firstweek]]
This is an exercise in dealing with data that tends to fall into a [http://en.wikipedia.org/wiki/Poisson_distribution| Poisson distribution]
==Poisson statistics second week==
<P>
We took data using one sweep through 256 channels with dwell times of 10ms, 20ms, 40ms, 80ms, 100ms, 200ms, 400ms, 800ms, 1s, and 10s.
[http://www-hep.phys.unm.edu/~gold/phys307L/manual.pdf Quick link to lab manual here]
===data files===
==Lab equipment and setup==
I will post the ascii files I got here for instructional purposes
The scintillator and PMT are one unit. It looks like a large flashlight. The scintillator is a large Thallium doped Sodium Iodide crystal in the wider end of the detector device. What the scintilator does is emit a pulse of ultraviolet light every time it absorbs high energy electromagnetic or particle radiation. Read more about [http://en.wikipedia.org/wiki/Scintillator scintillators] and [http://en.wikipedia.org/wiki/Scintillation_%28physics%29 scintillation]. The burst of UV light has an energy roughly proportional to the energy of whatever radiation the scintilator absorbed, but this isn't important in this experiment.


The PMT is on the other end of the detector. Read about [http://en.wikipedia.org/wiki/Photomultiplier Photomultiplier tubes]. The PMT will produce a current roughly proportional to the energy of the ultraviolet light falling on its photocathode. How proportional and by what proportion aren't of much importance here either, since we are using the device to count events, not to measure their energy.
10ms
<pre><nowiki>Oct 03 2007    03:07:41 am    Elt: 000000 Seconds. Real Time: 000000


The PMT needs a high voltage power source to turn the pulses of light hitting its photocathode into currents, usually in the range of 1000V to 2000V. Increased voltage usually corresponds to increased sensitivity. The point of this exercise is to find what the probability of distribution function of an unlikely event looks like, so throughout the experiment, use a voltage of 1000V.  To further decrease the number of events detected, keep the detector in a lead brick "cave."
ID: No spectrum identifier defined


The output of the PMT can either come from the anode or dynode connectors at the back of the PMT. Output after the PMT has detected a light burst will be a positive voltage spike coming out of the dynode and a negative spike coming out of the anode. This is important because the event counter needs pulses of an amplitude larger than the PMT produces and the amplifier has settings for amplification of either positive or negative pulses. Try to look at the output of the PMT with an oscilloscope to make sure whether the pulses are positive or negative. The pulses are sharp, meaning they are short time wise and may be difficult to see, and come at random intervals, meaning triggering on the oscilloscope may not act nice. For this part you may want turn the high voltage up to 2000V and take the detector apparatus out of the cave so that the detector detect more events per second. This will make the pulses easier to see on the oscilloscope.
Memory Size: 16384 Chls  Conversion Gain: 1024  Adc Offset: 0000 Chls


The counter itself will be a MCA (MultiChannel Analyzer) card inside the computer next to the rest of the equipment. The card has an input suited for connecting to a coax cable connector, so that you can run a coax from the amplifier to the back of the computer without any special connectors. A MCA normally counts and bins incoming voltage pulses according to pulse height (Pulse Height mode, PHA), but for this exercise you will want to use the card in MCS mode (Multi Channel Scaling), where it will count and bin according to when the pulses arrive. to do this, short the MCA/REJ to the SCA pin on the back of the card using a "hydra" breakout cable. If you are unsure if the MCA card is ready to take data or where the hell the hydra or the MCA card is, ask Dr. Koch or myself for help.


To set up dwell times and the number of passes for data collection, go to the program's main menu>setup(or press S-there is some way of getting a noise pointer with that thing but I never learned what button to push to get it)>MCS. To set up the channel number look around for some submenu called something like memory settings, hit enter, and a bunch of fractions (powers of 2), select one of them, select a memory subgroup(which one doesn't matter as far as I've been able to tell) Dr. Gold's lab manual recommends 256 channels. If the number of channels isn't correct, pick a smaller or larger fraction of the memory.


Start data acquisition with F1. The program stops after doing a single sweep through all the channels. (count and bin events into channel x for d dwell time, count and bin events into channel x+1 for d dwell time,...). Pressing F1 again stops acquisition and control+F2 or something like that erases your data.
Chn    Counts  ROI


Save your data in an ASCII file (File>save as ASCII or something)
  0,        0, 000


Data will be saved in folder C:\PCA3. Pressing the windows button on the keyboard will minimize the program so that you can hunt around for your file.
  1,        0, 000


Erase your data, rinse, wash repeat until sufficient data is taken.
  2,       0, 000


==final graph==
  3,        0, 000
Shows the Probability distribution function as is drifts from a Poisson distribution of a rarely occuring event to the more familiar Gaussian
 
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</nowiki></pre>
other files
*[[Image:Dwell20ms.asc ]]
*[[Image:Dwell40ms.asc]]
*[[Image:Dwell80ms.asc]]
*[[Image:Dwell100ms.asc]]
*[[Image:Dwell200ms.asc]]
*[[Image:Dwell400ms.asc]]
*[[Image:Dwell800ms.asc]]
*[[Image:Dwell1s.asc]]
*[[Image:Dwell10s.asc]]
==Octave script for analysis & plotting==
chop off the first ten lines of the data files before use
 
rename variable and filenames as necessary. Probably only needs minor tweeks so that it can run in matlab.
<pre><nowiki>#octave
#a sort of diary of commands to automate the data processing day to day
#some of octave's functions do not work in matlab,and vice
#versa. Some functions may work, but not in the same way
#Most things work, though.
 
#Tomas Mondragon
 
load LAB3_D10.ASC;    #The load function is intended
load LAB3_D20.ASC;    #to read files saved by octave.
load LAB3_D40.ASC;    #Load can also read .mat files,
load LAB3_D80.ASC;    #but only versions saved by matlab
load LAB3_D100.ASC;    #version 4. It can also read text files.
load LAB3_D200.ASC;    #if no variable name info is in the file
load LAB3_D400.ASC;    #it names the variable after the file name
load LAB3_D800.ASC;
load LAB3_D1S.ASC;
load LAB3_D10S.ASC;
 
dwell10ms=LAB3_D10(:,2);    #load put what was on LAB3_D10.ASC into the
dwell20ms=LAB3_D20(:,2);    #variable LAB3_D10. I only care about the second
dwell40ms=LAB3_D40(:,2);    #column of the data, so here I extract it to an
dwell80ms=LAB3_D80(:,2);    #appropriately named variable.
dwell100ms=LAB3_D100(:,2); 
dwell200ms=LAB3_D200(:,2);  #The first column of LAB3_D10 is the channel number
dwell400ms=LAB3_D400(:,2);  #but a more appropriate name would be the bin index.
dwell800ms=LAB3_D800(:,2);  #The second column is a count of how many events fell
dwell1s=LAB3_D1S(:,2);      #into the bin. The third column isn't important for this
dwell10s=LAB3_D10S(:,2);    #experiment, but knowing what it is will allow one to take
                            #advantage PCA3's region of interest feature. It just marks
                            #where one has marked ROI's. for example, 4 indicates the bin or channel
                            #lies within ROI 4
 
#idea:use max(dwell10s) as max bin for all so grapfh are same x scale
 
#[y,x]=hist(data,bincenters) is a a function that sets up bins whose values are centered
#at the values given by the vector bincenter and counts the number of times the values in
#data fall into each of the bins. y contains the frequncy counts and x contains the
#corresponding bin index. bar(x,y) will plot the histogram of data. In general, x=bincenters,
#so one can shorten [y,x]=hist(data,bincenters) to y=hist(data,bincenters) and use
#bar(bincenters,y) to plot the same thing.
 
bincenters=0:max(dwell10s);
 
freq10ms=hist(dwell10ms,bincenters);
freq20ms=hist(dwell20ms,bincenters);
freq40ms=hist(dwell40ms,bincenters);
freq80ms=hist(dwell80ms,bincenters);
freq100ms=hist(dwell100ms,bincenters);
freq200ms=hist(dwell200ms,bincenters);
freq400ms=hist(dwell400ms,bincenters);
freq800ms=hist(dwell800ms,bincenters);
freq1s=hist(dwell1s,bincenters);
freq10s=hist(dwell10s,bincenters);
 
#plots. press any key to move to next plot
bar(bincenters,freq10ms)
title("frequency counts for dwell time=10ms")
xlabel("number of events occuring during dwell time")
ylabel("frequency count")
pause
replot
bar(bincenters,freq20ms)
title("frequency counts for dwell time=20ms")
%xlabel("number of events occuring during dwell time")
%ylabel("frequency count")
pause
replot
bar(bincenters,freq40ms)
title("frequency counts for dwell time=40ms")
%xlabel("number of events occuring during dwell time")
%ylabel("frequency count")
pause
replot
bar(bincenters,freq80ms)
title("frequency counts for dwell time=80ms")
%xlabel("number of events occuring during dwell time")
%ylabel("frequency count")
pause
replot
bar(bincenters,freq100ms)
title("frequency counts for dwell time=100ms")
%xlabel("number of events occuring during dwell time")
%ylabel("frequency count")
pause
replot
bar(bincenters,freq200ms)
title("frequency counts for dwell time=200ms")
%xlabel("number of events occuring during dwell time")
%ylabel("frequency count")
pause
replot
bar(bincenters,freq400ms)
title("frequency counts for dwell time=400ms")
%xlabel("number of events occuring during dwell time")
%ylabel("frequency count")
pause
replot
bar(bincenters,freq800ms)
title("frequency counts for dwell time=800ms")
%xlabel("number of events occuring during dwell time")
%ylabel("frequency count")
pause
replot
bar(bincenters,freq1s)
title("frequency counts for dwell time=1s")
%xlabel("number of events occuring during dwell time")
%ylabel("frequency count")
pause
replot
bar(bincenters,freq10s)
title("frequency counts for dwell time=10s")
%xlabel("number of events occuring during dwell time")
%ylabel("frequency count")
 
</nowiki></pre>
==Compilation of plots==
The plot that the script above produced with the data I obtained are shown in sequence below. As the dwell time increases, the most often occurring event count increases and the frequency vs. event count plots drift from something resembling a poisson distibution of a rarely occuring event to a gaussian distribution
[[Image:Poissongraphsanimation.gif]]
[[Image:Poissongraphsanimation.gif]]
[[Physics307L:People/Mondragon/Notebook/071010 | SECOND WEEK(data and processing)]]

Revision as of 16:49, 17 October 2007

firstweek

Poisson statistics second week

We took data using one sweep through 256 channels with dwell times of 10ms, 20ms, 40ms, 80ms, 100ms, 200ms, 400ms, 800ms, 1s, and 10s.

data files

I will post the ascii files I got here for instructional purposes

10ms 

Oct 03 2007     03:07:41 am     Elt: 000000 Seconds.  Real Time: 000000

ID: No spectrum identifier defined

Memory Size: 16384 Chls  Conversion Gain: 1024  Adc Offset: 0000 Chls



 Chn    Counts  ROI

   0,        0, 000

   1,        0, 000

   2,        0, 000

   3,        0, 000

   4,        0, 000

   5,        0, 000

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other files

Octave script for analysis & plotting

chop off the first ten lines of the data files before use

rename variable and filenames as necessary. Probably only needs minor tweeks so that it can run in matlab. 

#octave
#a sort of diary of commands to automate the data processing day to day
#some of octave's functions do not work in matlab,and vice 
#versa. Some functions may work, but not in the same way
#Most things work, though.

#Tomas Mondragon

load LAB3_D10.ASC;     #The load function is intended
load LAB3_D20.ASC;     #to read files saved by octave.
load LAB3_D40.ASC;     #Load can also read .mat files,
load LAB3_D80.ASC;     #but only versions saved by matlab
load LAB3_D100.ASC;    #version 4. It can also read text files.
load LAB3_D200.ASC;    #if no variable name info is in the file
load LAB3_D400.ASC;    #it names the variable after the file name
load LAB3_D800.ASC;
load LAB3_D1S.ASC;
load LAB3_D10S.ASC;

dwell10ms=LAB3_D10(:,2);     #load put what was on LAB3_D10.ASC into the 
dwell20ms=LAB3_D20(:,2);     #variable LAB3_D10. I only care about the second
dwell40ms=LAB3_D40(:,2);     #column of the data, so here I extract it to an
dwell80ms=LAB3_D80(:,2);     #appropriately named variable. 
dwell100ms=LAB3_D100(:,2);   
dwell200ms=LAB3_D200(:,2);   #The first column of LAB3_D10 is the channel number
dwell400ms=LAB3_D400(:,2);   #but a more appropriate name would be the bin index.
dwell800ms=LAB3_D800(:,2);   #The second column is a count of how many events fell
dwell1s=LAB3_D1S(:,2);       #into the bin. The third column isn't important for this
dwell10s=LAB3_D10S(:,2);     #experiment, but knowing what it is will allow one to take 
                             #advantage PCA3's region of interest feature. It just marks
                             #where one has marked ROI's. for example, 4 indicates the bin or channel 
                             #lies within ROI 4

#idea:use max(dwell10s) as max bin for all so grapfh are same x scale

#[y,x]=hist(data,bincenters) is a a function that sets up bins whose values are centered 
#at the values given by the vector bincenter and counts the number of times the values in 
#data fall into each of the bins. y contains the frequncy counts and x contains the 
#corresponding bin index. bar(x,y) will plot the histogram of data. In general, x=bincenters,
#so one can shorten [y,x]=hist(data,bincenters) to y=hist(data,bincenters) and use 
#bar(bincenters,y) to plot the same thing.

bincenters=0:max(dwell10s);

freq10ms=hist(dwell10ms,bincenters);
freq20ms=hist(dwell20ms,bincenters);
freq40ms=hist(dwell40ms,bincenters);
freq80ms=hist(dwell80ms,bincenters);
freq100ms=hist(dwell100ms,bincenters);
freq200ms=hist(dwell200ms,bincenters);
freq400ms=hist(dwell400ms,bincenters);
freq800ms=hist(dwell800ms,bincenters);
freq1s=hist(dwell1s,bincenters);
freq10s=hist(dwell10s,bincenters);

#plots. press any key to move to next plot
bar(bincenters,freq10ms)
title("frequency counts for dwell time=10ms")
xlabel("number of events occuring during dwell time")
ylabel("frequency count")
pause
replot
bar(bincenters,freq20ms)
title("frequency counts for dwell time=20ms")
%xlabel("number of events occuring during dwell time")
%ylabel("frequency count")
pause
replot
bar(bincenters,freq40ms)
title("frequency counts for dwell time=40ms")
%xlabel("number of events occuring during dwell time")
%ylabel("frequency count")
pause
replot
bar(bincenters,freq80ms)
title("frequency counts for dwell time=80ms")
%xlabel("number of events occuring during dwell time")
%ylabel("frequency count")
pause
replot
bar(bincenters,freq100ms)
title("frequency counts for dwell time=100ms")
%xlabel("number of events occuring during dwell time")
%ylabel("frequency count")
pause
replot
bar(bincenters,freq200ms)
title("frequency counts for dwell time=200ms")
%xlabel("number of events occuring during dwell time")
%ylabel("frequency count")
pause
replot
bar(bincenters,freq400ms)
title("frequency counts for dwell time=400ms")
%xlabel("number of events occuring during dwell time")
%ylabel("frequency count")
pause
replot
bar(bincenters,freq800ms)
title("frequency counts for dwell time=800ms")
%xlabel("number of events occuring during dwell time")
%ylabel("frequency count")
pause
replot
bar(bincenters,freq1s)
title("frequency counts for dwell time=1s")
%xlabel("number of events occuring during dwell time")
%ylabel("frequency count")
pause
replot
bar(bincenters,freq10s)
title("frequency counts for dwell time=10s")
%xlabel("number of events occuring during dwell time")
%ylabel("frequency count")

Compilation of plots

The plot that the script above produced with the data I obtained are shown in sequence below. As the dwell time increases, the most often occurring event count increases and the frequency vs. event count plots drift from something resembling a poisson distibution of a rarely occuring event to a gaussian distribution

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