2014

Welcome to Advanced Physics Lab 2014!

Instructors

Professor: Tobias Marriage (marriage@pha.jhu.edu), Office: Bloomberg 215 (Office Hours: Thu 4:30-6:30)

TAs: Ian Anderson (ianderso@pha.jhu.edu), Office: Bloomberg 429

Lab Guru: Steve Wonnell (wonnell@pha.jhu.edu), Office: Bloomberg 478

Wiki

This page (https://wiki.pha.jhu.edu/advlab_wiki/index.php/2014) is your source of much course-related knowledge. Useful materials will be distributed here.

Also check out general descriptions of labs and pages for previous years at

https://wiki.pha.jhu.edu/advlab_wiki/index.php

General Description

In this class, you you'll learn

• how to conduct an experiment, collecting data with special attention to estimating systematic and statistical measurement errors,
• how to model the data, propagating errors to model parameters,
• and how to present your work through scientific writing.

These three aspects essentially define the course. Each lab will be evaluated based on how well the three aspects are realized.

Classes

Times: Monday 10:00-12:50 and 1:30-4:20

Classes will run in a seminar format. They will begin with a lecture, sometimes involving computing/laptop participation, followed by discussion and short updates on experimental work from students. A shared online directory (Google drive) will be used to upload update material.

At the beginning of a new experiment, an introduction to the new experiment will also be given in the class.

Experiments

While much effort will be spent with data analysis and scientific writing, the exciting centerpiece of the course is executing four advanced experiments. You'll have a generous three weeks to work on each experiment. The first experiment will start in the second week of the course. The planned experiments we will work through are the following.

1. Brownian Motion: The goal of this experiment is to estimate the Boltzmann constant using a measurement of the Brownian motion of microscopic spheres.
2. Speed of Light: In this experiment you use the classic Foucault spinning mirror measurement to estimate the speed of light.
3. Single Photon Interference: This experiment explores the interference of quantum wave functions.
4. Radio Astronomy: In this experiment you'll use a 1.4 GHz (21 cm) radio telescope to look out into the galaxy and (possibly) beyond.

The first two experiments are intended to be a good match to the introductory data analysis being taught in lectures and readings during the first half of the course. These should help you ramp up in terms of your familiarity with data and errors. They are also very cool measurements of two of the most important numbers in physics!

The second two experiments, to be completed in the second half of the semester, are intended to be matched to the more advanced material presented in lectures and readings during the second half of the course. These are new experiments that we've developed over the last two years and think are particularly exciting.

Location and Access

The all experiments associated with the lab are located in room 478 of Bloomberg Hall: the Physics Undergraduate Computer (PUC) lab. Access to the lab is managed by Steve Wonnell (wonnell@pha.jhu.edu). A PUCLab login for the computers will also be useful. Steve Wonnell is also the person to contact for this.

Safety

Use your common sense in all situations. In these labs you'll encounter lasers (wear appropriate goggles) and other manageable hazards. Follow the safety instructions at all times. 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 the professor, Steve Wonnell or the TA.

Readings

I will lecture weekly from the classic Bevington text, which is mainly on data analysis. In principle you can work from lecture notes, but you will get more out of the class if you make an effort to read the text and take your own notes.

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

Below is the nominal schedule for the course.

Date	Lecture	Weekly Reading	Other Notes
Jan 27	Class Overview, Measurement & Errors	Bev. Ch1	Work on basic LaTeX and Python examples
Feb 03	Probability Distributions, Exp. 1 Introduction	Bev. Ch 2	Start Exp. 1; LaTeX Tutorial
Feb 10	Propagation of Errors	Bev. Ch 3	Updates on Exp. 1; Exp. 1 Draft 1 Due; Coding Tutorial
Feb 17	Method of Maximum Likelihood	Bev. Ch 4	Updates on Exp. 1; Exp. 1 Draft 2 Due
Feb 24	Student's t and Chi-sq Distributions	Bev. Ch 4	Exp. 1 Report Due, Start Exp. 2
Mar 3	Linear Least Squares 1	Bev. Ch 6	Updates on Exp. 2; Exp. 2 Draft 1 Due; Snow Cancellation
Mar 10	Review Line Fitting	Bev. Ch 6	Updates on Exp. 2; Exp. 2 Draft 2 Due
Mar 17-21 Spring Break
Mar 24	General Linear Least Squares	Bev. Ch 7	Exp. 2 Due, Start Exp. 3
Mar 31	Nonlinear Fitting	Bev. Ch 8	Updates on Exp. 3; Exp. 3 Draft 1 Due
Apr 7	Experiment/Analysis Topic Review		Updates on Exp. 3; Exp. 3 Draft 2 Due
Apr 14	Done with Analysis topics!		Exp. 3 Due, Start Exp. 4
Apr 21	TBD	-	Updates on Exp. 4; Exp. 4 Draft 1 Due
Apr 28	TBD	-	Updates on Exp. 4; Exp. 4 Draft 2 Due

Groups and Scheduling

You should team up into lab groups of two or three for executing the experiments. It's probably best to have one group through all labs, so you become used to working with one another and fall into a schedule. It would be good to form these in the first week.

While the first lab has multiple stations, the following labs have one apparatus. Therefore groups will need to schedule times throughout week when they will be using the instrument. We will set up a scheduling system (likely Google calendar or spreadsheet). Groups may "team up" if necessary to collect data together, but preferably it can be done group by group. In any case, just make sure everyone gets to take some data! And also be friendly and considerate when sharing the equipment.

Grading

Grades breakdown as

• 80% Labs
• 20% Preparation (weekly updates and submission of drafts)

Each lab grade will be divided into three equal sections: experiment execution (20 pts), data analysis (20 pts), and presentation (20 pts).

Collaboration Policy

Execution of the experiment is a group effort, so is necessarily collaborative. 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 Reports

Final Reports

Work should be submitted by email in PDF format to the professor (marriage@pha.jhu.edu) and TA (ianderso@pha.jhu.edu). To help us organize, the subject of the email should be "Advanced Lab: [last name]" where [last name] is your last name.

The reports are due by midnight on the day before the next lab begins. The grade of late reports will be multiplied by exp(-(days late)/7), where days late can be fractional (starting from midnight).

In this class it's crucial not to procrastinate.

All reports will be returned within 2 weeks from the submission date.

Participation

Update material (plots etc) for discussion should be uploaded to the shared drive before class.

As required, you should submit drafts of your report with introduction and procedure after the first week and through data analysis by the end of the second week of the lab. Drafts should be submitted in the same way described above for the final report, though the filename should indicate that it's a draft to avoid confusion.

Anatomy of an Experiment

Experiment Execution. The first step in executing an experiment is to have a good idea of the phenomenon being measured -- the reason why you're doing the experiment. Then you need to have a thorough knowledge of the experimental apparatus. With this preparation you will be able to take data. But obtaining measured values is not enough. You need both values and errors. You need to conduct the experiment in a way that estimates systematic errors and statistical errors. Systematic errors can be checked for by conducting the experiment in more than one way that should, e.g., give the same result and checking for discrepancies. Statistical errors may be obtained by repeating the experiment and evaluating the sample variance of the data or there might be an analytic expectation for the statistical error, as in the case of counting experiments.

Data Analysis and Interpretation. The input to data analysis consists of measured values and their errors. You then fit this data with some physical model. If the fit is "good", then you can believe the best-fit model parameters and associated model parameter errors. These model parameters tell you something about the physical world.

Presentation Lab reports constitute the language of the course. The sections of a report are

• Abstract -- Summarily say the aim of the experiment and what you used to measure the phenomenon. Then quote your result which is usually some physical parameter with errors.
• Introduction -- Describe qualitatively the phenomenon being measured and the history of the measurements and theory behind the current experiment (seminal works cited etc). This should not contain much information about what you did in the experiment-- just roundly what you aim to do. The intro is mainly useful background and can be relatively brief.
• Theory -- Introduce quantitatively the physical effect that you're trying to probe. Introduce equations.
• Experiment Description and Data -- Describe the experiment setup and procedure. Also describe the data and errors. In particular you'll likely be giving the averages of your many samples collected and errors on those average. These should appear in a plot and, when possible, a table.
• Data Analysis -- Derive a theoretical interpretation from the data propagating errors to theoretical model parameters etc. If appropriate, discuss the "goodness" of the model fit.
• Discussion -- Interpret your results and discuss what may have gone wrong if, e.g., the fit in the Data Analysis section was not good.
• Conclusion -- A short section where you summarize the paper. This section could possibly include future directions to take the work.

Lab Report Specifications

The reports are to be created on a computer with computer generated graphics, plots, etc. The document preparation system for the reports is LaTeX. The computers in the PUC lab have various installations of LaTeX editors/compilers. You can also download freeware for your personal computers. I think the online editor "ShareLaTeX" is pretty good.

The lab reports should have an abstract, an introduction, a theory section, description of the experiment (apparatus and procedure) and reduced data and errors, description of the analysis of the data, discussion of results, a conclusion, and a bibliography.

The format should have 1" margins with no smaller than 11 point font. The maximum number of pages is 6, including figures and tables. Be concise.

A standard strategy is to create your figures first in order to guide the body of the text.

Other Useful 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

You might also find useful websites from previous years.

Tutorials: In particular, see the previous year's tutorials.

And of course... Wikipedia!