User:Brian P. Josey/Notebook/Junior Lab/2010/10/25: Difference between revisions
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Because she was sick the first week and I was sick the second, Kirstin and I will actually work from different data sets even though we worked together on the lab. So our individual data points will not be the same, but our end results should be very similar. | Because she was sick the first week and I was sick the second, Kirstin and I will actually work from different data sets even though we worked together on the lab. So our individual data points will not be the same, but our end results should be very similar. | ||
==Data Analysis== | |||
==Conclusion== | |||
==Acknowledgments and References== | |||
==NOTE TO SELF== | ==NOTE TO SELF== | ||
Revision as of 10:37, 7 November 2010
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Poisson DistributionThis 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  Because most of this experiment was performed on the computer, there was very little equipment needed for the experiment. The first piece of equipment was the combined scintillator-PMT. We used this to detect the background radiation in the lab. When the scintillator absorbs radiation, it would fire a beam of ultraviolet light down the tube to the PMT. The PMT, then creates a signal voltage that is picked up by a card in the computer. The card then sends this information to the UCS 30 software, that then counts the number of radiation events in a given window of time. We also used a Spectech Universal Computer Spectrometer power supply to give a bias voltage to the detector. This voltage determines the sensitivity of the detector. Set-Up and ProcedureThe set-up was exceptionally simple:
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:
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. Data and ResultsHere is the data that I collected. Each spreadsheet contains all the individual data points per window on the first page, then the maximum and minimum values, number of windows with a given number of events, averages, and errors on the second page of each table. How I calculated this is described below in the data analysis section, but I included it on these tables for simplicity. The tables are arranged in increasing window size, starting at 10 ms, and concluding with 1 s at the bottom. {{#widget:Google Spreadsheet |
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}} Because she was sick the first week and I was sick the second, Kirstin and I will actually work from different data sets even though we worked together on the lab. So our individual data points will not be the same, but our end results should be very similar. Data AnalysisConclusionAcknowledgments and ReferencesNOTE TO SELFBrian 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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