2016

Welcome to Advanced Physics Lab 2016!

Instructors

Professor: Tobias Marriage (marriage@jhu.edu)

Teaching Assistant: Devin Crichton (dcrichton@jhu.edu)

Lab Guru: Steve Wonnell (wonnell@jhu.edu)

Classes

Classes are held Monday 10:00-12:50 and 1:30-4:20 in the Physics Undergraduate Computer Lab (PUC). A login for the PUC Lab computers may also be useful. For access to the PUC Lab follow the instructions on this webpage: http://puclab.johnshopkins.edu/application.html.

The first half of class will be devoted to lecture and discussion. The second half of class will be devoted to work with hands-on help from instructors. For the hands-on part, laptops should be brought to every class. See the instructor if you need to borrow a laptop.

Essential Elements

In this class, you will conduct three experiments:

Brownian Motion (BM): The goal of this experiment is to estimate the Boltzmann constant using a measurement of the Brownian motion of microscopic spheres.

Speed of Light (SoL): In this experiment you use the classic "time of flight" measurement by Foucault to estimate the speed of light.

Galactic Rotation (GR): In this experiment you'll use a radio telescope to measure the rotational velocities of clouds of hydrogen in the Galactic disk. You then use the data to discriminate between models of the Milky Way with and without dark matter.

Each experiment may be divided conceptually into three parts: measurement, analysis, and presentation.

Measurement

The first step in the experiment is to carry out a measurement. This requires a thorough understanding of the experimental apparatus. For each experiment a variety and number of measurements are required to estimate the statistical and systematic error on the results.

Safety. Use your common sense in all situations. In these labs you'll encounter manageable hazards. Follow the instructions carefully. Food and drink are not allowed near the labs. Safety also follows from orderliness: please keep (and leave) the lab in an organized state. When in doubt, ask an instructor.

Analysis

Analysis is the evaluation of data towards an interpretation that accounts for errors in the data. Roughly speaking there will be three analysis steps in this class:

1. Process the measured data into a reduced dataset with errors,
2. interpret the reduced data in the context of a physical model, finding best estimates and errors on model parameters, and
3. assess the impact of systematic errors.

Python. The tool we will use for the analysis is the Python programming language and its numerical and scientific computing modules. Specifically you will use Ipython Notebook. We recommend the Anaconda Python distribution (or equivalent).

Presentation

For each experiment you will write a report. The reports will be presented in a standard scientific format with use of figures and tables. The format should have 1" margins with no smaller than 11 point font. The maximum number of pages is 6, including figures and tables. The quality of your scientific writing and other presentation elements is crucial. See the report checklist for guidelines when writing your reports.

LaTeX. The document preparation system for reports is LaTeX. You can also download LaTeX freeware for your personal computers (e.g., TeXworks on all platforms, TeXShop for Mac, TeXnicCenter for Windows). Good online LaTeX editors also exist (e.g., ShareLaTeX). The computers in the PUC lab have various installations of LaTeX editors/compilers.

Schedule

Each experiment is allotted approximately four weeks. You must manage your time during these four weeks effectively to succeed in this class. Here is an example schedule:

• Week 1: Make the measurement and write first sections of report
• Week 2: Analyze data, continue to write, retake data if needed
• Week 3: Continue to refine analysis, finish first draft of report
• Week 4: Revise analysis and report, submit the report on Friday

For all but the final experiment, after submission you will meet one-on-one with the instructor to review your work in preparation for the next experiment.

Week-by-Week

Date	1st Half of Class	2nd Half of Class and Homework
Jan 25	Snow Day	Install the Anaconda Python distribution. Install LaTeX or use an On-line LaTeX editor (ShareLaTeX).
Media:LaTeX_Example.txt, Python_Example
Feb 01	Class Overview; Measurement and Errors Concepts; Parent Distributions; BM Measurement Introduction	Verify Python and LaTeX; Start BM
Feb 08	Sample Distributions and Estimators; Outliers; BM Analysis I; LaTeX Tutorial; Python Tutorial 1	Work on BM
Feb 15	Snow Day	Work on BM
Feb 22	Error Propagation; Consistency and systematic error	Work on BM
Feb 29	BM Analysis Wrap-up/Review; Maximum Likelihood & Weighted Average	Work on BM; BM Work Due Friday March 4 (Extension) 5pm (Email TA)
Mar 7	SoL Introduction	Work on SoL
Spring Break
Mar 22	Fitting a line; Python Tutorial 2 Emailed	Work on SoL; BM Reviews
Mar 28	Chi-squared Goodness of Fit	Work on SoL; BM Reviews
Apr 4	SoL Analysis Review	Work on SoL; SoL Report Due Friday April 8 5pm (Email TA)
Apr 11	GR Introduction; Python Tutorial 3 Emailed	Work on GR; SoL Reviews
Apr 18	Generalized linear and non-linear fitting	Work on GR; SoL Reviews
Apr 25	GR Analysis Review	Work on GR; GR Work Due Friday May 6 5pm (Email TA)

Work Submission and Evaluation

Evaluation. Grades breakdown as 1/3 experiment execution, 1/3 data analysis, 1/3 scientific writing.

Collaboration. Students are encouraged to discuss experiments, analysis, and other course related issues with the instructors and their peers. However, each person should obtain their own data, perform their own data analysis (e.g., no code sharing), produce their own plots, and write their own report. For SoL, two students will need to collaborate to make the measurement, but each should obtain their own dataset.

Ethics. The strength of the university depends on academic and personal integrity. In this course, you must be honest and truthful. Ethical violations include cheating on exams, plagiarism, reuse of assignments, improper use of the Internet and electronic devices, unauthorized collaboration, alteration of graded assignments, forgery and falsification, lying, facilitating academic dishonesty, and unfair competition. For more info: http://e-catalog.jhu.edu/undergrad-students/student-life-policies/.

Submitting Reports and Notebooks. Reports should be submitted in PDF format to the TA through email together with iPython Notebooks containing the analysis and any data needed to run the notebook.

Late Policy. The grade of late work will decay exponentially according to the equation exp(-(days late)/7).

Other Resources

Data Analysis

Lectures on data analysis are loosely based on the text

• Bevington & Robinson, Data Reduction and Error Analysis for the Physical Sciences, 3rd Edition, McGraw-Hill, ISBN 0-07-247227-8, 2003

We have a couple class copies that you can read in the lab. Please keep these in the PUCLab.

Other Data analysis texts include

• Press, Teukolsky, Vetterling, Flannery, Numerical Recipes in C (Available online)
• Lupton, "Statistics in Theory and Practice"

LaTeX

• Lamport, LaTeX: A Document Preparation System
• A Not Too Short Introduction to LaTeX: media:not_too_short.pdf

Previous Year's Tutorials (No longer supported -- use at own risk!)

• Analysis 1: Mean, Variance, and Error Propagation
• Analysis 2: Goodness of Fit
• Analysis 3: Linear Model Fitting and Error Propagation
• Analysis 4: Linear Fit Example
• Analysis 5: Nonlinear Fit Example

And of course... Wikipedia!