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==Welcome to Advanced Physics Lab 2014!==
=Welcome to Advanced Physics Lab 2015!=


===Instructors===
==Instructors==


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


Lab Guru: Steve Wonnell (wonnell@pha.jhu.edu), Office: Bloomberg 478
Teaching Assistant: '''Devin Crichton''' (dcrichton@jhu.edu)


===Wiki===
Lab Guru: '''Steve Wonnell''' (wonnell@pha.jhu.edu)


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.
==The Advanced Lab Summary==


Also check out general descriptions of labs and pages for previous years at
In this class, you will conduct [[#Experiments|experiments]], [[#Analysis | analyze data]], and write up your results in [[#Reports|reports]].


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


===General Description===
'''The class times''' are Monday 10:00-12:50 and 1:30-4:20.


In this class, you you'll learn
'''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 [[#Instructors | Steve Wonnell]].
*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===
'''Laptops''' should be brought to class.


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


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.
'''The second half''' of class will be devoted to work with hands on help from instructors.


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


===Experiments===
'''[[Brownian Motion | Brownian Motion (BM)]]''': The goal of this experiment is to estimate the Boltzmann constant using a measurement of the Brownian motion of microscopic spheres.


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.
'''[[Speed of Light | Speed of Light (SoL)]]''': In this experiment you use the classic "time of flight" measurement by Foucault to estimate the speed of light.


#'''Brownian Motion''': The goal of this experiment is to estimate the Boltzmann constant using a measurement of the Brownian motion of microscopic spheres.
'''[[Radio Telescope | 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.
#'''Speed of Light''': In this experiment you use the classic Foucault spinning mirror measurement to estimate the speed of light.
#'''Single Photon Interference''': This experiment explores the interference of quantum wave functions.
#'''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!
===Raw Data Sets===


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.
'''Each student''' will collect their own data. For SoL, two students will need to collaborate, but each should obtain their own dataset.


====Location and Access====
'''Initial submission''' of data sets for evaluation occurs in the first week or two of the experiment.


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===


====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 [[#Instructors|instructor]].'''


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.
==Analysis==


===Readings===
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:


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.
#Process the [[Raw Data Sets|raw data sets]] into a reduced dataset with errors,
#Interpret the reduced data in the context of a physical model and
#Assess the impact of systematic errors.


Bevington & Robinson, ''Data Reduction and Error Analysis for the Physical Sciences'', 3rd Edition, McGraw-Hill, ISBN 0-07-247227-8, 2003
===Tool for Analysis===


I will have a couple class copies that you can read in the lab. (Please keep these in the PUCLab!)
The tool we will use for the analysis is the Python programming language and its numerical and scientific computing modules.


===Schedule===
You will submit an IPython Notebook with your [[Report|report]].


Below is the nominal schedule for the course.  
==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 [[media:Report_Checklist.pdf|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 [http://latex-project.org/ 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==


{| class="wikitable"; border="1"
{| class="wikitable"; border="1"
|-
|-
!Date !! Lecture  !! Weekly Reading !! Other Notes
!Date !! 1st Half of Class  !! Reading !! 2nd Half of Class and Homework
|-
!Jan 27
| Class Overview, Measurement & Errors || Bev. Ch1 || Work on basic LaTeX and Python examples
|-
| colspan="4" align="center" |  [[media:AdvLab2014Intro.pdf]] (See current wiki for updated course details.)
|-
|-
!Feb 03
!Jan 26
| Probability Distributions, Exp. 1 Introduction || Bev. Ch 2 || Start Exp. 1; LaTeX Tutorial
| Class Overview; Experiments, Measurement & Errors 1 || Bev. Ch1 || Install [http://ipython.org/install.html Ipython Notebook]; Install [http://latex-project.org/ LaTeX] or use On-line LaTeX editor. Consider reading ahead for [[#Experiments | Brownian Motion]].
|-
|-
| colspan="4" align="center" |   [[media:AdvLabBrownianMotion.pdf]], [[Latex Tutorial 2014]]
| colspan="4" align="center" | [[Media:LaTeX_Example.txt]], [[Python_Example]]  
|-
|-
!Feb 10
!Feb 02
| Propagation of Errors || Bev. Ch 3 || Updates on Exp. 1; Exp. 1 Draft 1 Due; Coding Tutorial
| 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)'''
|-
|-
| colspan="4" align="center" | [[python 101]], [[media:data_python_101.txt]], [[Image Processing]], [[Step Linking]], [[Mosaic]]
!Feb 09
| Probability Distributions; BM Analysis Discussion; Python Tutorial 1 || Bev. Ch 2 || Start analysis & writing initial sections of report
|-
|-
!Feb 17
| colspan="4" align="center" | [[Python_Tutorial_1]]
| Method of Maximum Likelihood || Bev. Ch 4 || Updates on Exp. 1; Exp. 1 Draft 2 Due
|-
|-
!Feb 24
!Feb 16
| Student's t and Chi-sq Distributions || Bev. Ch 4 || Exp. 1 Report Due, Start Exp. 2
| Propagation of Errors || Bev. Ch 3 || Working on Full Report
|-
|-
| colspan="4" align="center" | [[media:SpeedOfLightOverview.pdf]], [[Speed of Light Part 1]],  [[media:data_IA_far.txt]], [[media:data_TM_far.txt]]
!Feb 23
| Method of Maximum Likelihood || Bev. Ch 4 || '''BM Report Due Friday Feb 27 5pm (Email Prof)'''
|-
|-
!Mar 3
!Mar 2
| Linear Least Squares 1 || Bev. Ch 6 || Updates on Exp. 2; Exp. 2 Draft 1 Due; Snow Cancellation
| 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
|-
|-
| colspan="4" align="center" | [[media:lecture_notes_line_fit.pdf]], [[Speed of Light Part 2]], [[media:averages.txt]], [[media:SL_data_with_fit.png]]
| colspan="4" align="center" |  
|-
|-
!Mar 10
!Mar 9
| Review Line Fitting || Bev. Ch 6 || Updates on Exp. 2; Exp. 2 Draft 2 Due
| Linear Least Squares 1; SoL Analysis Discussion; Python Tutorial 2 || Bev. Ch 6 || BM Report Reviews
|-
|-
| colspan="4" align="center"| Mar 17-21 Spring Break
| colspan="4" align="center"| Mar 17-21 Spring Break
|-
|-
!Mar 24
!Mar 23
| General Linear Least Squares || Bev. Ch 7 || Exp. 2 Due, Start Exp. 3
| Review Line Fitting || Bev. Ch 7 || BM Report Reviews
|-
|-
| colspan="4" align="center" | [[media:SPIOverview.pdf]], [[media:spi_indistinguishable.txt]],  [[SPI Data Reading and Linear Fit]]
!Mar 30
| General Linear Least Squares  || || '''SoL Report Due Friday April 3 5pm (Email Prof)'''
|-
|-
!Mar 31
!Apr 6
| Nonlinear Fitting || Bev. Ch 8 || Updates on Exp. 3; Exp. 3 Draft 1 Due
| Nonlinear Fitting 1; GR Introduction  || Bev. Ch 8 || Schedule SoL Report Reviews
|-
|-
| colspan="4" align="center" | [[SPI Data Reading, Linear, and Non-linear Fit]]
|-
|-
!Apr 7
!Apr 13
| Experiment/Analysis Topic Review || || Updates on Exp. 3; Exp. 3 Draft 2 Due
| Nonlinear Fitting 2; Python Tutorial 3 || Bev. Ch 8  || '''Submit 1st GR Data by Sun Apr 19 11:59 pm (GoogleForm)'''; SoL Report Reviews
|-
|-
!Apr 14
| colspan="4" align="center" | [[Python_Tutorial_3]]
| Done with Analysis topics! || || Exp. 3 Due, Start Exp. 4
|-
|-
| colspan="4" align="center" | [[media:cmd.txt]], [[media:g40.txt]], [[media:AdvLabRadioTelescopeIntroWithRef.pdf]], [[Rotation Curve Slides]], [[media:AdvLab SRT instrumentation.pdf]], [[Read SRT Data and Plot]]
!Apr 20
| Experiment/Analysis Topic Review || - || SoL Report Reviews
|-
|-
!Apr 21
!Apr 27
| TBD || - || Updates on Exp. 4; Exp. 4 Draft 1 Due
| Reserved for Overflow || - || '''GR lab due on Friday May 1 5pm (Email Prof)'''
|-
| colspan="4" align="center" |  [[media:g30.txt]], [[media:g60.txt]], [[media:g90.txt]], [[media:reference.txt]], [[Cmd File Generator]], [[SRT Data Analysis 2]]
|-
!Apr 28
| TBD || - || Updates on Exp. 4; Exp. 4 Draft 2 Due
|}
|}


===Groups and Scheduling===
==Grading==


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


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.
==Collaboration Policy==


===Grading===
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.


Grades breakdown as
===Ethics===
 
*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
The strength of the university depends on academic and personal integrity. In this
Line 155: Line 162:
falsification, lying, facilitating academic dishonesty, and unfair competition. For more info: http://e-catalog.jhu.edu/undergrad-students/student-life-policies/.
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===
==Work Submission and Late Work==


====Final Reports====
===Submitting Reports and Notebooks===


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.
Reports should be submitted in PDF format to the professor through email together with iPython Notebooks containing the analysis.


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).
===Submitting Raw Data===


In this class it's crucial not to procrastinate.  
Initial submission of the raw data will be done through google drive or email depending on the lab.  


All reports will be returned within 2 weeks from the submission date.
If your initial dataset is flawed, you will have the opportunity to retake and resubmit it for partial credit.


====Participation====
===Late Policy===


Update material (plots etc) for discussion should be uploaded to the shared drive before class.  
The grade of late work will be multiplied by exp(-(days late)/7), where days late can be fractional (starting from midnight).  


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===
===Anatomy of an Experiment===
Line 196: Line 203:


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


===Other Useful Resources===
==Other Resources==


'''Data Analysis'''
'''Data Analysis'''
*Press, Teukolsky, Vetterling, Flannery, ''Numerical Recipes in C'' (Available online)
*Press, Teukolsky, Vetterling, Flannery, ''Numerical Recipes in C'' (Available online)
*Lupton, "Statistics in Theory and Practice"
*Lupton, "Statistics in Theory and Practice"
Line 207: Line 214:
*Lamport, ''LaTeX: A Document Preparation System''
*Lamport, ''LaTeX: A Document Preparation System''
*A Not Too Short Introduction to LaTeX: [[media:not_too_short.pdf]]
*A Not Too Short Introduction to LaTeX: [[media:not_too_short.pdf]]
 
<!--You might also find useful [[Main_Page#Syllabus_and_Extra_Information| websites from previous years]].-->
You might also find useful [[Main_Page#Syllabus_and_Extra_Information| websites from previous years]].
'''Previous Year's Tutorials'''
 
* [[Analysis 1 | Analysis 1: Mean, Variance, and Error Propagation]]
'''Tutorials''': In particular, see [[2013#Tutorials|the previous year's tutorials]].
* [[Analysis 2 | Analysis 2: Goodness of Fit]]
* [[Analysis 3 | Analysis 3: Linear Model Fitting and Error Propagation]]
* [[ Analysis 4 | Analysis 4: Linear Fit Example]]
* [[ Analysis 5 | Analysis 5: Nonlinear Fit Example]]


And of course... Wikipedia!
And of course... Wikipedia!

Latest revision as of 02:52, 14 April 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
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
Python_Tutorial_3
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

Previous Year's Tutorials

And of course... Wikipedia!

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