C:\>prompt. This is advanced lab, so you will use an advanced operating system called DOS. Type
dirand hit enter to list the folders in the C drive. Navigate to the Counts folder by typing
cd counts(and pressing enter). Then type
counts.exeand hit enter to start up the counting program. Press A to begin the first program, which counts the number of events in a fixed time interval. Enter plateau.txt as the file name, set number of repetitions to 10 Take a range of voltages (about 20 should be fine) from 900 - 1200 and plot log(counts/min) vs voltage applied. Take at least 10 repetitions per voltage setting. This should be easy with the program. Insert a floppy disk into the computer, then enter
copy plateau.txt A:\at the prompt. Once the green light on the floppy drive turns off (meaning it has finished copying), then eject the disk and put it into the USB floppy drive attached to one of the computers on the central table. On that computer, navigate to the A dive and copy the text file(s) onto that computer for further analysis.
# Import the python data analysis module import pandas as pd # Read your data into a DataFrame construct, and assign names to # the columns. The column t1 is the times between each count, and # t2 is just the same as t1, but shifted one row upwards df = pd.read_csv('path_to_your_data.txt', delim_whitespace = True, names = ['Index', 't1', 't2', index_col = 'index'] # Print the first five rows of the dataframe so we know what our # data looks like! print(df[0:5]) # Create a new column, 't3', which is simply the sum of t1 and t2 - # That is, t3 contains the times between every two counts. df['t3'] = df['t1']+df['t2'] df.hist(column = 't1', bins = 100)
Your resulting histogram should look something like this:
However, don’t stop here! You should make the histogram of the distribution between every count as well, and compare it to the theoretical distribution that you would expect from your knowledge of counting statistics. Plot the theoretical function and estimate how good the experimental data matches the theory. If you see some discrepancies, try to reason about why they might arise.
Things I am looking for in particular - an intelligent discussion of why we see the distributions of ‘time between counts’ and ‘time between every 2 counts’ look the way they do.