2018

Welcome to Advanced Physics Lab 2018!

Instructors

Professor: Tobias Marriage (marriage@jhu.edu)

Teaching Assistant: David Ely (dely4@jhu.edu)

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

Learning Assistants: James Damewood (Section 1) and Chenxiao Zeng (Section 2)

Classes

Classes are Monday 10:00-12:50 and 1:30-4:20 in the Physics Undergraduate Computer (PUC) Lab. You will need access to the PUC Lab outside of class hours (using our JHU ID card) to complete experiments. 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 Jupyter Notebooks. 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 report should be as concise as possible, approximately 6 pages or less, including figures and tables. The quality of your scientific writing and other presentation elements is crucial. See the media:Report_Checklist_2018.pdf for guidelines when writing your reports.

LaTeX. The document preparation system for reports is LaTeX. If you are new to LaTeX, an online editor is recommended for this class (e.g., ShareLaTeX or Overleaf). You can also download LaTeX freeware for your personal computers (e.g., TeXworks on all platforms, TeXShop for Mac, TeXnicCenter for Windows). 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: Research the measurement, make the measurement, and the write first sections of report
• Week 2: Analyze data, continue to write, and retake data if needed
• Week 3: Continue to refine analysis; finish first draft of report
• Week 4: Revise analysis and report; submit the report

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. For the first experiment, you will be given an opportunity to resubmit a corrected analysis and rewrite of the report.

Week-by-Week

Date	1st Half of Class	2nd Half of Class and Homework
Jan 29	Welcome. Measurement and Errors; Distributions and Estimators; Lecture Notes	LaTeX Tutorial (youtube, mp4,tex, pdf, fig.png) and Python Tutorial 1 (youtube, mp4, pynb).
Feb 05	Brownian Motion (BM) Introduction. Media:BM_Overview_2018.pdf	BM Week 1
Feb 12	Outliers; Monte Carlo; Error Propagation; Lecture Notes	BM Week 2
Feb 19	Consistency and systematic errors; Weighted average Lecture Notes	BM Week 3
Feb 26	Brownian Motion Wrap-up Lecture Notes	Upload BM by end of Sunday March 4
Mar 05	Speed of Light (SoL) Introduction Media:SpeedOfLightOverview_2018.pdf	SoL Week 1; Python Tutorial 2 (youtube, mp4, pynb, data.txt)
Mar 12	Maximum Likelihood; Fitting a line	SoL Week 2
Spring Break
Mar 26	Chi-squared Goodness of Fit	SoL Week 3
Apr 02	SoL Wrap-up	Upload SoL and BM Revision by Sunday April 8
Apr 09	Galaxy Rotation (GR) Introduction	GR Week 1; Python Tutorial 3 (youtube, mp4, pynb, data.txt); Radio Telescope Read Example (pynb, g30_example.txt )
Apr 16	Generalized linear and non-linear fitting	GR Week 2 reference_example.txt
Apr 23	GR Wrap-up	GR Week 3
Apr 30		Upload GR by end of Friday May 4

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 BM and SoL, two students may 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 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 will be uploaded (upload request links to be provided) in PDF format 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!