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@pha.jhu.edu)
The Advanced Lab Summary
In this class, you will conduct experiments, analyze data, and write up your results in reports.
Classes
The class times are Monday 10:00-12:50 and 1:30-4:20.
The class location is the Physics Undergraduate Computer lab (PUClab). A PUCLab login for the computers may also be useful. Access to the lab and computers is managed by Steve Wonnell.
Laptops should be brought to class.
The first half of class will be devoted to lecture and discussion of that week's topics. 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.
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.
Raw Data Sets
Each student will collect their own data. For SoL, two students will need to collaborate, but each should obtain their own dataset.
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 raw data sets 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.
Tool for Analysis
The tool we will use for the analysis is the Python programming language and its numerical and scientific computing modules.
You will submit an IPython Notebook with your report.
Reports
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.
More information can be found in the report checklist.
Feedback
For the first two reports, students will meet one-on-one with the professor to discuss their graded work.
Tool for Reports
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.
Readings
Lectures will be based on
Bevington & Robinson, Data Reduction and Error Analysis for the Physical Sciences, 3rd Edition, McGraw-Hill, ISBN 0-07-247227-8, 2003
I will have a couple class copies that you can read in the lab. Please keep these in the PUCLab.
Schedule
| Date | 1st Half of Class | Reading | 2nd Half of Class and Homework |
|---|---|---|---|
| Jan 25 | Class Overview; Experiments, Measurement & Errors 1 | Bev. Ch1 | Install Ipython Notebook; Install LaTeX or use On-line LaTeX editor. |
| Media:LaTeX_Example.txt, Python_Example | |||
| Feb 01 | Measurement and Errors 2; BM Introduction; LaTeX Tutorial | Bev. Ch 1&2 | Start BM |
| Feb 08 | Probability Distributions; BM Analysis Discussion; Python Tutorial 1 | Bev. Ch 2 | Start analysis & writing initial sections of report |
| Feb 15 | Propagation of Errors | Bev. Ch 3 | Working on Full Report |
| Feb 22 | Method of Maximum Likelihood | Bev. Ch 4 | BM Work Due Friday Feb 26 5pm (Email TA) |
| Feb 29 | Student's t and Chi-sq Distributions; SoL Introduction | Bev. Ch 6 | Start SoL; Schedule BM Report Reviews |
| Mar 7 | Linear Least Squares 1; SoL Analysis Discussion; Python Tutorial 2 | Bev. Ch 6 | BM Report Reviews |
| Spring Break | |||
| Mar 22 | Review Line Fitting | Bev. Ch 7 | BM Report Reviews |
| Mar 28 | General Linear Least Squares | SoL Report Due Friday April 1 5pm (Email TA) | |
| Apr 4 | Nonlinear Fitting 1; GR Introduction | Bev. Ch 8 | Start GR; Schedule SoL Report Reviews |
| Apr 11 | Nonlinear Fitting 2; Python Tutorial 3 | Bev. Ch 8 | SoL Report Reviews |
| Apr 18 | Experiment/Analysis Topic Review | - | SoL Report Reviews |
| Apr 25 | Reserved for Overflow | - | GR lab due on Friday May 1 5pm (Email TA) |
Grading
Grades breakdown as 1/3 experiment execution, 1/3 data analysis, 1/3 scientific writing.
Collaboration Policy
Students are encouraged to discuss experiments, analysis, and other course related issues with the instructors and their peers. However, each person should carry out their own measurement, data analysis (e.g., no code sharing), produce their own plots, and write their own report.
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/.
Work Submission and Late Work
Submitting Reports and Notebooks
Reports should be submitted in PDF format to the professor through email together with iPython Notebooks containing the analysis.
Submitting Raw Data
Initial submission of the raw data will be done through google drive or email depending on the lab.
If your initial dataset is flawed, you will have the opportunity to retake and resubmit it for partial credit.
Late Policy
The grade of late work will be multiplied by exp(-(days late)/7), where days late can be fractional (starting from midnight).
Other Resources
Data Analysis
- 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!