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 first half of the 10am and 1:30am classes will be similar. 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 in the Galactic disk and judge whether the data better fits a model with or without dark matter.
Each experiment may be divided 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:
- Process the measured data into a reduced dataset with errors,
- Interpret the reduced data in the context of a physical model, finding best estimates and errors on model parameters, and
- 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.
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, papeeria). 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.
- 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 | Class Overview; Measurement and Errors | Install Ipython Notebook; Install LaTeX or use On-line LaTeX editor. |
| Media:LaTeX_Example.txt, Python_Example | ||
| Feb 01 | Distributions and Estimators; BM Introduction; LaTeX Tutorial | Start BM |
| Feb 08 | Error Propagation; BM Analysis; Python Tutorial 1 | Work on BM |
| Feb 15 | Outliers, Consistency, Maximum Likelihood | Work on BM |
| Feb 22 | Review; Fitting a line | Work on BM; BM Work Due Friday Feb 26 5pm (Email TA) |
| Feb 29 | Chi-square Goodness of Fit; SoL Introduction; Python Tutorial 2 | Start SoL; Schedule BM Reviews |
| Mar 7 | SoL Analysis | Work on SoL; BM Reviews |
| Spring Break | ||
| Mar 22 | Analysis Review | Work on SoL; BM Reviews |
| Mar 28 | General Linear Least Squares 1 | Work on SoL; SoL Report Due Friday April 1 5pm (Email TA) |
| Apr 4 | General Linear Least Squares 2; GR Introduction | Start GR; Schedule SoL Reviews |
| Apr 11 | Nonlinear Fitting; GR Analysis; Python Tutorial 3 | Work on GR; SoL Reviews |
| Apr 18 | Experiment/Analysis Topic Review | Work on GR; SoL Reviews |
| Apr 25 | Work on GR; GR Work Due Friday May 1 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, 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 be multiplied by exp(-(days late)/7).
Other Resources
Data Analysis
Readings
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!