Experimental Data Analysis Lab

Lab Goals

The goals of this lab are to explore the statistical properties of counting random events.

Lab Manifest

• Radioactive samples
• Vernier Digital Radiation Monitors (Geiger tubes)
• LoggerPro and LabPro (for data acquisition)
• Lab Handout
• Lab 4 data taking videos
• Data files

General Instructions

The lab handout, linked above, gives detailed instructions for this lab. There are four main tasks for this lab, which can be done separately.

The first task is to measure background levels with the Geiger counter and establish that the distribution of events seen in a given time interval follows a Poisson distribution. The second task is to verify the Gaussian approximation with width given by sqrt(n) by increasing the counting rate using a radioactive source. Third, we want to verify the inverse square law for the flux of particles through the detector. Finally, the attenuation of ionizing radiation through material will be measured.

Remote Data Access

Because taking data for this lab in person will be difficult, and the data taking process is not really that interesting anyways, we have taken the data for you and have made this available under the Lab 4 data directory. There are also a series of short videos available on Canvas that illustrate how this data was recorded. Please watch these videos during your lab sessions while reading along in the lab writeup. You will be expected to describe the data taking process in your Jupyter notebook just as if you had taken the data yourself.

Code Assignment

While I prefer to see all of your code in your Jupyter notebook, the most important code that I will be grading you on is detailed here. I would like to see the code you used to make an overlay of a poisson function to your histogram of count rates in section 4.5. Also, you need to perform some linear fits to extract parameters with uncertainties for both the inverse square law and the attenuation length. You may either write your own fitting function using the equations in Taylor, or you can use the polyfit function from numpy. You don't need to use the errors to weight your data in your linear fit, although you may if you wish. You do need to extract the uncertainties on the fit parameters, however, and make a plot with the data and best fit line included. These figures should be produced in your Jupyter notebook. The input data for these figures should be read from the input data files provided, although if you prefer to enter it by hand into an array based on the output of some previous analysis, that is also acceptable.

Errata

For reference, the Users Guide for the Digital Radiation Monitors are available from Vernier.