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Stony Brook course CEB558/PHY315: Hands-On Science with Cosmic Rays

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Week 9: April 1, 2008: Technology Fair (and more analyses)

I wasn't at class this week. Instead, I borrowed a detector and a Geiger counter and went to a Technology Fair at Smithtown High School. The intent of the fair was to show students that there are jobs in technology-related fields. There were several exhibitors, including Motorola, Grumman, Verizon, IBM and others, and a couple of the school departments had displays. Our science department had the cosmic ray poster that I worked on last week and equipment, and some moving critters under a microscope, projected onto a TV. Here is a picture of me with our cosmic ray poster:

It had been a while since I did any work on my project, so during the week (after the Technology Fair) I took another look at the data. Well, first I e-mailed Joe Willie, who visited MARIACHI in January, and asked him about the pressure dependence that he had found for his cosmic ray data. He said that he had found results similar to those at the University of Adelaide, where they found -0.2% (decrease) in cosmic ray counts per millibar atmospheric pressure. He also said that he used as much data as possible, about 1 month of data, to find the pressure dependence. He has a lot more information here, on Joe Willie's page.

The graph of cosmic ray counts vs. pressure is from Excel, where the best linear fit to the data is given by the equation:

Counts = -0.03406 * pressure + 5355

This gives a decrease in countrate of 0.18% for an increase of 1 millibar of atmospheric pressure. That's just about the same (within the 1 significant figure, anyway) as Joe Willie's and U of Adelaide's results. In this equation, count is in average counts per minute (on our large scintillation detectors, but not corrected for efficiency). The pressure is given in Pa. Joe Willie used pressure in millibars, and a millibar is the same as a hectopascal (hPa), which is a common unit for measuring atmospheric pressure. 1 millibar = 1hPa = 100 Pa.

So now I have an equation for the best linear fit between barometric pressure and count rate. I subtracted to find the difference between the best fit line and the actual value for each count rate, and graphed the difference. The graph looks pretty much the same as the earlier graph of Counts * (p/po)^2.

Difference between count rate and linear fit to pressure.

Count rate normalized by a factor of (p/po)^2.

Go on to next week's class.