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Teaching Assistant: '''Devin Crichton''' (dcrichton@jhu.edu)
Teaching Assistant: '''Devin Crichton''' (dcrichton@jhu.edu)


Lab Guru: '''Steve Wonnell''' (wonnell@pha.jhu.edu)
Lab Guru: '''Steve Wonnell''' (wonnell@jhu.edu)
 
==The Advanced Lab Summary==
 
In this class, you will conduct [[#Experiments|experiments]], [[#Analysis | analyze data]], and write up your results in [[#Reports|reports]].


==Classes==
==Classes==


'''The class times''' are Monday 10:00-12:50 and 1:30-4:20.
Classes are held Monday 10:00-12:50 and 1:30-4:20 in the Physics Undergraduate Computer Lab (PUC). 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 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]].
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.


'''Laptops''' should be brought to class.
==Essential Elements==


'''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.
In this class, you will conduct three experiments:
 
'''The second half''' of class will be devoted to work with hands-on help from instructors.
 
==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.
'''[[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.
Line 31: Line 23:
'''[[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.
'''[[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.


'''[[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.
'''[[Radio Telescope | 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.


===Raw Data Sets===
Each experiment may be divided conceptually into three parts: measurement, analysis, and presentation.


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


===Safety===
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.


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]].'''
'''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]].


==Analysis==
===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:  
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|raw data sets]] into a reduced dataset with errors,
#Process the measured data 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
#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.
#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|report]].
'''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 Ipython Notebook. We recommend the [https://www.continuum.io/downloads Anaconda Python distribution] (or equivalent).


==Reports==
===Presentation===
   
   
For each experiment you will write a report.  
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. The quality of your scientific writing and other presentation elements is crucial. See the [[media:Report_Checklist_2016.pdf|report checklist]] for guidelines when writing your reports.


The reports will be presented in a standard scientific format with use of figures and tables.
'''LaTeX.''' 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., [https://www.sharelatex.com ShareLaTeX]). The computers in the PUC lab have various installations of LaTeX editors/compilers.


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


More information can be found in the [[media:Report_Checklist.pdf|report checklist]].
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:


===Feedback===
*Week 1: Make the measurement and write first sections of report
*Week 2: Analyze data, continue to write, retake data if needed
*Week 3: Continue to refine analysis, finish first draft of report
*Week 4: Revise analysis and report, submit the report on Friday


For the first two reports, students will meet one-on-one with the professor to discuss their graded work.
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.
 
===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==


===Week-by-Week===
{| class="wikitable"; border="1"
{| class="wikitable"; border="1"
|-
|-
!Date !! 1st Half of Class  !! Reading !! 2nd Half of Class and Homework
!Date !! 1st Half of Class  !! 2nd Half of Class and Homework
|-
|-
!Jan 25  
!Jan 25  
| 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.
| Snow Day || Install the [https://www.continuum.io/downloads Anaconda Python distribution]. Install [http://latex-project.org/ LaTeX] or use an On-line LaTeX editor ([https://www.sharelatex.com ShareLaTeX]).
|-
|-
| colspan="4" align="center" | [[Media:LaTeX_Example.txt]], [[Python_Example]]  
| colspan="3" align="center" | [[Media:LaTeX_Example.txt]], [[Python_Example]]  
|-
|-
!Feb 01
!Feb 01
| Measurement and Errors 2; BM Introduction; LaTeX Tutorial || Bev. Ch 1&2 || Start BM
| Class Overview; Measurement and Errors Concepts; Parent Distributions; BM Measurement Introduction || Verify Python and LaTeX; Start BM
|-
|-
!Feb 08
!Feb 08
| Probability Distributions; BM Analysis Discussion; Python Tutorial 1 || Bev. Ch 2 || Start analysis & writing initial sections of report
|Sample Distributions and Estimators; Outliers; BM Analysis I;  LaTeX Tutorial; Python Tutorial 1 || Work on BM
 
|-
|-
!Feb 15
!Feb 15
| Propagation of Errors || Bev. Ch 3 || Working on Full Report
| Snow Day || Work on BM
|-
|-
!Feb 22
!Feb 22
| Method of Maximum Likelihood || Bev. Ch 4 || '''BM Work Due Friday Feb 26 5pm (Email TA)'''
| Error Propagation; Consistency and systematic error  || Work on BM
|-
|-
!Feb 29
!Feb 29
| Student's t and Chi-sq Distributions; SoL Introduction || Bev. Ch 6 ||  Start SoL; Schedule BM Report Reviews
| BM Analysis Wrap-up/Review; Maximum Likelihood & Weighted Average ||  Work on BM; '''BM Work Due Friday March 4 (Extension) 5pm (Email TA)'''
|-
|-
!Mar 7
!Mar 7
| Linear Least Squares 1; SoL Analysis Discussion; Python Tutorial 2 || Bev. Ch 6 || BM Report Reviews
| SoL Introduction || Work on SoL
|-
|-
| colspan="4" align="center"| Spring Break
| colspan="3" align="center"| Spring Break
|-
|-
!Mar 22
!Mar 22
| Review Line Fitting || Bev. Ch 7 || BM Report Reviews
| Fitting a line; Python Tutorial 2 Emailed || Work on SoL; BM Reviews
|-
|-
!Mar 28
!Mar 28
| General Linear Least Squares  || || '''SoL Report Due Friday April 1 5pm (Email TA)'''
| Chi-squared Goodness of Fit || Work on SoL; BM Reviews
|-
|-
!Apr 4
!Apr 4
| Nonlinear Fitting 1; GR Introduction  || Bev. Ch 8  ||  Start GR; Schedule SoL Report Reviews
| SoL Analysis Review || Work on SoL; '''SoL Report Due Friday April 8 5pm (Email TA)'''
|-
|-
|-
!Apr 11
!Apr 11
| Nonlinear Fitting 2; Python Tutorial 3 || Bev. Ch 8  ||  SoL Work Reviews
| GR Introduction; Python Tutorial 3 Emailed ||  Work on GR; SoL Reviews
 
|-
|-
!Apr 18
!Apr 18
| Experiment/Analysis Topic Review || - || SoL Report Reviews
| Generalized linear and non-linear fitting || Work on GR; SoL Reviews
|-
|-
!Apr 25
!Apr 25
| Reserved for Overflow || - || '''GR Work Due Friday May 1 5pm (Email TA)'''
| GR Analysis Review  || Work on GR; '''GR Work Due Friday May 6 5pm (Email TA)'''
|}
|}


==Grading==
==Work Submission and Evaluation==


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


==Collaboration Policy==
'''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 SoL, two students will need to collaborate to make the measurement, but each should obtain their own dataset.


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
 
===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
course, you must be honest and truthful. Ethical violations include cheating on
exams, plagiarism, reuse of assignments, improper use of the Internet and electronic
exams, plagiarism, reuse of assignments, improper use of the Internet and electronic
Line 156: Line 121:
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 Work==
'''Submitting Reports and Notebooks.''' Reports should be submitted in PDF format to the TA through email together with iPython Notebooks containing the analysis and any data needed to run the notebook.


===Submitting Reports and Notebooks===
'''Late Policy.''' The grade of late work will decay exponentially according to the equation exp(-(days late)/7).


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


===Submitting Raw Data===
'''Data Analysis'''
 
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).


<!--
Lectures on data analysis are loosely based on the text


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


'''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.
We have a couple class copies that you can read in the lab. Please keep these in the PUCLab.


'''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.
Other Data analysis texts include


'''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 Resources==
'''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"

Latest revision as of 23:23, 9 April 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@jhu.edu)

Classes

Classes are held Monday 10:00-12:50 and 1:30-4:20 in the Physics Undergraduate Computer Lab (PUC). 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 Ipython Notebook. 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 maximum number of pages is 6, including figures and tables. The quality of your scientific writing and other presentation elements is crucial. See the report checklist for guidelines when writing your reports.

LaTeX. 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). 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: Make the measurement and write first sections of report
  • Week 2: Analyze data, continue to write, retake data if needed
  • Week 3: Continue to refine analysis, finish first draft of report
  • Week 4: Revise analysis and report, submit the report on Friday

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.

Week-by-Week

Date 1st Half of Class 2nd Half of Class and Homework
Jan 25 Snow Day Install the Anaconda Python distribution. Install LaTeX or use an On-line LaTeX editor (ShareLaTeX).
Media:LaTeX_Example.txt, Python_Example
Feb 01 Class Overview; Measurement and Errors Concepts; Parent Distributions; BM Measurement Introduction Verify Python and LaTeX; Start BM
Feb 08 Sample Distributions and Estimators; Outliers; BM Analysis I; LaTeX Tutorial; Python Tutorial 1 Work on BM
Feb 15 Snow Day Work on BM
Feb 22 Error Propagation; Consistency and systematic error Work on BM
Feb 29 BM Analysis Wrap-up/Review; Maximum Likelihood & Weighted Average Work on BM; BM Work Due Friday March 4 (Extension) 5pm (Email TA)
Mar 7 SoL Introduction Work on SoL
Spring Break
Mar 22 Fitting a line; Python Tutorial 2 Emailed Work on SoL; BM Reviews
Mar 28 Chi-squared Goodness of Fit Work on SoL; BM Reviews
Apr 4 SoL Analysis Review Work on SoL; SoL Report Due Friday April 8 5pm (Email TA)
Apr 11 GR Introduction; Python Tutorial 3 Emailed Work on GR; SoL Reviews
Apr 18 Generalized linear and non-linear fitting Work on GR; SoL Reviews
Apr 25 GR Analysis Review Work on GR; GR Work Due Friday May 6 5pm (Email TA)

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 SoL, two students will 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 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/.

Submitting Reports and Notebooks. Reports should be submitted in PDF format to the TA through email together 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

Previous Year's Tutorials (No longer supported -- use at own risk!)

And of course... Wikipedia!

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