User:Brian P. Josey/Notebook/Junior Lab/2010/10/25: Difference between revisions

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==Poisson Distribution==
==Poisson Distribution==
For now, my lab partner is sick, and I am doing this lab on my own. The one that I am doing is the Poisson distribution lab. The Poisson distribution is used when a process occurs randomly, but at an average rate. Nuclear and subatomic processes, like emitting of radiation, are a good example of this. Here we are measuring the background radiation and showing that it satisfies the Poisson distribution. To measure it, we have a scintillator and PMT plugged into a computer. The scintillator will detect when some radiation occurs. This will fire ultraviolet radiation at the PMT, which will generate a small voltage. A card in the computer will measure this, and the software will count up all of the instances that this occurs at, and will serve as the source of the data.
This week, my lab partner, [[User:Kirstin Grace Harriger|Kirstin]], and I did the Poisson distribution experiment. This is a fairly straight forward experiment that is used to demonstrate the Poisson distribution. The Poisson distribution is used to describe when an event that occurs at random times independent of the last occurrence, but with an overall average rate. Examples of when this can be useful is counting the radiation off of a sample, or the number of births per day in a maternity ward. For this experiment, we counted the number of background radiation events in the lab. We used a combined scintillator-PMT to detect the events, and counted them using the UCS 30 software on the computer. From this, we were able to generate a series of data sets that contained the number of events in a given window of time, and then analyze them.


==Equipment==
==Equipment==

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Poisson Distribution

This week, my lab partner, Kirstin, and I did the Poisson distribution experiment. This is a fairly straight forward experiment that is used to demonstrate the Poisson distribution. The Poisson distribution is used to describe when an event that occurs at random times independent of the last occurrence, but with an overall average rate. Examples of when this can be useful is counting the radiation off of a sample, or the number of births per day in a maternity ward. For this experiment, we counted the number of background radiation events in the lab. We used a combined scintillator-PMT to detect the events, and counted them using the UCS 30 software on the computer. From this, we were able to generate a series of data sets that contained the number of events in a given window of time, and then analyze them.

Equipment

Since most of this lab is done on the computer, we have very little equipment. The first piece is a Spectech Universal Computer Spectrometer power supply, UCS 30. The second is the combined scintillator and PMT, model number #####. The software that I used is the Spectrum Techniques UCS30 software on the computer.

Set-Up

The set-up was exceptionally simple:

  1. . turn on the Spechtech
  2. . double click on the icon for the software on the desktop of the computer

Procedure

Like the set up, the procedure is pretty basic, the only issue is that the user interface on the computer doesn't make much sense. To set up the data collection, you want to set the cut off voltage fairly high before collecting the data. So the step by step process for collecting data is as follows:

  1. Under mode select "PHA (Amp In)"
  2. Under Settings Select "High Voltage On", and set it to an appropriate value, we used 1200 V. This value is used to adjust the sensitivity of the detector.
  3. Under mode, select "MCS (Internal)"
  4. Under Settings, select MCS, and then pick your appropriate dwell time, which is how large each bin is for the number of events counted.
  5. To collect data, hit the green "Go button" and let it run its course
  6. When it stops, save it to a file or USB drive, but save it as a "comma separated variable (*.csv)"
  7. Import it into Google docs

From this point, the procedure is actually in the data analysis. We did every single dwell time between 10 ms and 1 second. The values for these are then, 10, 20, 40, 80, 100, 200, 400, 800 ms and 1 s.

NOTE TO SELF

Brian P. Josey 17:41, 25 October 2010 (EDT) Koch suggested that you could set up a random Poisson distribution on your computer to compare to the official data, as a way to compare with what we've done so far. It might be a good way to really learn the Poisson for your own good.



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