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I. Error Analysis Notes

II. Error Analysis Exercise

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Contents 1 Note 2 References 3 Introduction 4 Problem Set 4.1 Problem 1 4.2 Problem 2 4.3 Problem 3 4.4 Problem 4 4.5 Problem 5

Note

All new Physics 111 Advanced Lab students are required to complete this assignment at the beginning of the semester. It will be graded on a P/NP basis; a late turn-in is allowed only with the instructor's approval before the due date. Don't jeopardize your grade on your first experiment by being late with this assignment. You need to know how to handle errors before you start a laboratory experiment.

View the video introduction to error analysis. The Error Analysis Excercise due date is Advanced Lab Report Due Dates.

References

1. P. Bevington, '"Data Reduction and Error Analysis for the Physical Sciences", McGraw-Hill. [An old standard that is pretty dry but straightforward. Chapter 5 is particularly important.]
2. J. R. Taylor, "An Introduction to Error Analysis:

[Taylor Book 2ed.

The Study of Uncertainties in Physical Measurements, 2nd Edition", University Science Books (1996). [A good introduction that is easy to follow.]

1. A. C. Melissinos and J. Napolitano, "Experiments in Modern Physics, 2nd Edition", Academic Press (2003).
2. W. H. Press, et al., "Numerical Recipes in C: The Art of Scientific Computing, 2nd Edition", Cambridge University Press (1992); refer to Ch. 14—"Modeling of Data". [The Numerical Recipes in Pascal or FORTRAN books contain identical information. This book is the standard reference for doing scientific work on computers. Chapter 14 has a good introduction to the method of maximum likelihood, chi–square fitting, modeling data in general, error estimates of fit parameters, and, important for later experiments, the Monte Carlo method of simulation.]

Introduction

In the 111-lab, the experiment does not end when you have finished collecting your data. In many labs, you will be required to perform a detailed analysis of the data you have acquired. The point of any scientific experiment is to make quantitative statements about the properties of the physical world. A common question is, are your measurements consistent with a particular theory or not? This question can only be answered by careful analysis, including both systematic uncertainties and statistical error.

The goals of this exercise are twofold. One is to familiarize students with the basics of error analysis. Ideally, this will serve as a guide during the acquisition and analysis of data throughout the advanced lab. The second goal is to introduce students to the Matlab numerical computing environment, which you will be using throughout the semester.

Before starting on EAX, please look over the Intro to Matlab section.

Problem Set

Problem 1

We want to measure the specific activity (number of decays per second) of a radioactive source so that we can use it to calibrate the equipment of the gamma-ray experiment. We use an electronic counter and a timer to measure the number of decays in a given time interval. In round numbers we measure 1000 decays in 5 minutes of observation. For how long should we measure in order to obtain a specific activity with a precision of 1%? Explain.

Problem 2

You are given two measurements of distance and their associated uncertainties: and . Calculate the propagated uncertainty in the (a) total distance A + B, (b) difference A - B, (c) the perimeter 2A + 2B and (d) the area .

Problem 3

You are given a dataset (Media:Peak.zip) from a gamma-ray experiment consisting of ~1000 hits. For each hit, the energy of the gamma-ray is recorded. We will assume that the energies are randomly distributed about a common mean, and that each hit is uncorrelated to others. Read the dataset from the enclosed file and:

1. Produce a histogram of the distribution of energies. Choose the number of bins wisely, i.e. so that the width of each bin is smaller than the width of the peak, and at the same time so that the number of entries in the most populated bin is relatively large. Since this plot represents randomly-collected data, plotting error bars would be appropriate (hint: use errorbar function in Matlab)
2. Compute the mean and RMS (standard deviation) of the distribution of energies and their statistical uncertainties
3. Fit the distribution to a Gaussian function, and compare the parameters of the fitted Gaussian with the mean and RMS computed above
4. How consistent is the distribution with a Gaussian ? In other words, compare the histogram from (1) to the fitted curve, and compute a goodness-of-fit value, such as χ² / df

Problem 4

In the optical pumping experiment we measure the resonant frequency as a function of the applied current (local magnetic field). Consider a mock data set:

Current I (Amps)	0.0	0.2	0.4	0.6	0.8	1.0	1.2	1.4	1.6	1.8	2.0	2.2
Frequency f (MHz)	0.14	0.60	1.21	1.94	2.47	3.07	3.83	4.16	4.68	5.60	6.31	6.78

1. Plot a graph of the pairs of values. Assuming a linear relationship between I and f, determine the slope and the intercept of the best-fit line using the least-squares method with equal weights, and draw the best-fit line through the data points in the graph.
2. From what s/he knows about the equipment used to measure the resonant frequency, your lab partner hastily estimates the uncertainty in the measurement of f to be σ_f = 0.01 MHz. Estimate the probability that the straight line you found is an adequate description of the observed data if it is distributed with the uncertainty guessed by your lab partner. (Use Matlab to calculate it or look it up in a table. For example, see table C-4 in Bevington). What can you conclude from these results? Repeat the analysis assuming your partner estimated the uncertainty to be σ_f = 1 MHz. What can you conclude from these results?
3. Assume that the best-fit line found in the previous exercise is a good fit to the data. Estimate the uncertainty in measurement of y from the scatter of the observed data about this line. Again, assume that all the data points have equal weight. Use this to estimate the uncertainty in both the slope and the intercept of the best-fit line. This is the technique you will use in the Optical Pumping lab to determine the uncertainties in the fit parameters.
4. Now assume that the uncertainty in each value of f grows with f: σ_f = 0.03 + 0.03 * f (MHz). Determine the slope and the intercept of the best-fit line using the least-squares method with unequal weights (weighted least-squares fit)

Problem 5

1. In the muon lifetime experiment we obtain a histogram for the decay rate as a function of the time after the muon enters the detector and announces its presence. We expect the distribution (the histogram) to be described by an exponential function. Rather than fitting with an exponential function, it is more convenient to plot the logarithm of the decay rate as a function of time and then fit a straight line to it. Since each data point (x_i,y_i) has a statistical error, σ_i, associated with it, what happens to these errors when the semi-log histogram (x_i,logy_i) is plotted? Explain and illustrate.
2. In a separate experiment, you want to calculate the energy, E₀ (eV), at which a certain distribution drops to zero. Your experiment shows that the distribution goes to zero at roughly . What is the energy cutoff and experimental bounds?