2015

Welcome to Advanced Physics Lab 2015!

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.

Initial submission of data sets for evaluation occurs in the first week or two of the experiment.

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 raw data sets into a reduced dataset with errors,
2. Interpret the reduced data in the context of a physical model and
3. 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 26	Class Overview; Experiments, Measurement & Errors 1	Bev. Ch1	Install Ipython Notebook; Install LaTeX or use On-line LaTeX editor. Consider reading ahead for Brownian Motion.
Media:LaTeX_Example.txt, Python_Example
Feb 02	Measurement and Errors 2; BM Introduction; LaTeX Tutorial	Bev. Ch 1&2	Submit 1st BM data by Sun Feb 08 11:59 pm (Email TA)
Feb 09	Probability Distributions; BM Analysis Discussion; Python Tutorial 1	Bev. Ch 2	Start analysis & writing initial sections of report
Python_Tutorial_1
Feb 16	Propagation of Errors	Bev. Ch 3	Working on Full Report
Feb 23	Method of Maximum Likelihood	Bev. Ch 4	BM Report Due Friday Feb 27 5pm (Email Prof)
Mar 2	Student's t and Chi-sq Distributions; SoL Introduction	Bev. Ch 6	Submit 1st SoL Data by Sun Mar 8 11:59 pm (GoogleForm); Schedule BM Report Reviews
Mar 9	Linear Least Squares 1; SoL Analysis Discussion; Python Tutorial 2	Bev. Ch 6	BM Report Reviews
Mar 17-21 Spring Break
Mar 23	Review Line Fitting	Bev. Ch 7	BM Report Reviews
Mar 30	General Linear Least Squares		SoL Report Due Friday April 3 5pm (Email Prof)
Apr 6	Nonlinear Fitting 1; GR Introduction	Bev. Ch 8	Schedule SoL Report Reviews
media:GRIntroduction2015.pdf
Apr 13	Nonlinear Fitting 2; Python Tutorial 3	Bev. Ch 8	Submit 1st GR Data by Sun Apr 19 11:59 pm (GoogleForm); SoL Report Reviews
media:Python_Tutorial_3_Data.txt
Apr 20	Experiment/Analysis Topic Review	-	SoL Report Reviews
Apr 27	Reserved for Overflow	-	GR lab due on Friday May 1 5pm (Email Prof)

Grading

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

Collaboration Policy

Execution of the experiment may be done in collaboration with others. Furthermore, students are encouraged to discuss experiments, analysis, and other course related issues with their peers (and, of course, with the instructors). However, each person should carry out their own 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

• 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!