The focus for the first quarter of PHYS 211 is to learn the fundamentals of experimental physics in a step-by-step manner. The course works through a set of broad experiments that are broken down into guided tutorials and short assignments with an emphasis on scaffolded learning and repeated opportunities for practice.

The expected learning outcomes for Autumn Quarter PHYS 21101 are broken into four categories, as follows.

Experimental Process

• Students are able to keep a lab notebook which provides a full record of their work on an experiment.
• Students are able to decide for themselves how to collect data, and are able to make real-time decisions about next steps based on interpretation of that data.
• Students are able to estimate statistical uncertainties in both measured and calculated quantities.
• Students are able to identify systematic bias in experiments and apply techniques to correct for or account for bias (qualitatively or quantitatively).
• Students are comfortable working with apparatus commonly encountered in experimental physics, and can understand their inherent biases and limitations.

Data Analysis

• Students are able to process and analyze data in order to extract quantities of interest, test models, or study new behaviors.
• Students are able to iterate on an experiment and use the results of their analysis to determine areas for improvement.

Scientific Communication

• Students are able to appropriately present results.
• Students are able to appropriately discuss results.

Drawing Conclusions

• Students are able to draw conclusions that are supported by their results and the correct interpretation of their data.

More information on each of these learning objectives can be found on the PHYS 211 Learning Objectives page.

Each quarter, students will attend a series of lectures or workshops offered by the lab staff and the faculty instructor for the course. Attendance at lectures is expected (unless prior arrangements have been made with the course instructors), and there will often be assignments associated with these sessions. Assignments may include pre-lecture reading or writing prompts, post-lecture reflections, participation in activities at the lecture, or short exercises or problems.

Lectures are held in KPTC 120 on Mondays from 3:30-4:20 pm.

Each quarter, students will work on a series of experimental projects. In-lab work is done during scheduled 4-hour lab periods, but there will also be substantial out-of-lab work required (in the form of preparation, meetings with TAs and/or group members, analysis, and writing).

Students will work in pairs when possible. Lab partners will share data and are welcome to talk and share ideas, but are expected to do independent analysis and submit independent assignments.

Each project will have the following outline:

• Pre-lab: Look over your assigned experiment wiki page and read any suggested material (e.g. the “Theory” section).
  • Each lab includes a prelab exercise that must be submitted before the start of their lab section. Late assignments will not be accepted!
  • These assignments will be graded simply as “complete” or “incomplete”. Feedback will be given in-person at the start of lab. There are no opportunities to resubmit or correct the prelab exercises.
  • Students may need to do research beyond what is provided on the wiki page.
• In-lab Work: Groups will meet one day a week for a 4-hour lab session. Each lab will extend over a number of consecutive weeks.
  • Students are expected to arrive on time and stay the full period (unless they have made prior arrangements with the instructors and with their partner).
  • Students must keep a lab notebook.
    • A bound, paper notebook is standard, but a digital lab notebook is allowed (so long as it allows a student to record notes, calculations, drawings, figures, etc… just as one can do on paper).
      • Excel and Google Docs are useful tools, but they are NOT digital notebooks and cannot be used in place of a proper notebook.
    • While the notebook will not be graded on its own, many of the in-lab checkpoints will require students to show work from the notebook while discussing results with TAs or instructors.
  • Students should not wait until the end of the experiment to start doing the analysis!
    • Students should begin analyzing the data (and even formatting and working on the out-of-lab assignments) as soon as possible.
    • There will be specific analysis pieces due each week, but students are welcome to (and encouraged) to work further ahead as soon as they have the data needed.
• In-lab Checkpoints: There will be a number of in-lab checkpoints that students are required to complete and talk about with the instructors or TAs.
  • These assignments are not graded, but students are expected to complete them and discuss with a TA while in the lab. Feedback will be given on the spot.
  • If a particular checkpoint exercise is deemed unsatisfactory, students may continue working on the exercise and ask to discuss again (with as many opportunities as time allows).
• Out-of-lab Assignments: There will be a number of out-of-lab analysis assignments (which instructors will sometimes refer to collectively as the “report”, even though that's not a great description).
  • There will typically be a specific set of analysis tasks to complete each week. By dividing the analysis up into weekly chunks, the hope is that the workload will be more spread out and that students will get more frequent feedback (while there is still time to use it in the lab.)
  • Analysis assignments must be submitted by 11:59 pm on the due date (which is typically the day before a student returns to lab).
    • Late assignments will be given a penalty. (See below for details).
  • These assignments will be graded on the quality of the work and the argument.
    • If a student has questions about the expectation(s), they should reach out to the TA for clarification.
  • TAs will not regrade assignments. Students are expected to submit their best work the first time.

Lab sessions are held in KPTC 005/009. Groups meeting on Wednesday or Friday meet from 1:30 to 5:20 pm. Groups meeting on Tuesday or Thursday meet from 2:00 to 5:50 pm. (See course calendar for specific dates.)

In Autumn Quarter PHYS 21101, the breakdown is as follows:

• Critical Potentials Analysis – 50 points
  • Analysis Day 1
  • Analysis Day 2
• Gamma Cross Sections Analysis – 100 points
  • Analysis Day 1
  • Analysis Day 2
  • Analysis Day 3
  • Analysis Day 4
• Exercises and Tutorials – 50 points
  • Exercise: Syllabus quiz
  • Exercise: Uncertainties
  • Exercise: Discussion of Results
  • Exercise: Reflection Activity
  • Tutorial: Python
  • Tutorial: LaTeX
  • Prelab: Critical Potentials
  • Prelab: Gamma Cross Sections

Out-of-lab analysis assignments are due by 11:59 pm the day before the next day in lab. Work will be accepted late, but will receive a penalty of 5% per day (up to a maximum of 3 days, or 15%). After 3 days, students must meet with lab instructors to discuss the situation before continuing with the course.

If a student needs extra time to complete their out-of-lab assignment, they may use grace days to extend their deadline.

• Students are given 2 grace days per quarter.
• A grace day is one, indivisible 24-hour period, starting at the due time.
• Unused grace days DO NOT roll over from one quarter to the next.

All late coursework must be submitted before the start of Finals Week in order to allow TAs to complete grading without interfering with their own class and final exam schedules. Late work will not be accepted after this date without prior arrangement. If you believe you have extenuating circumstances and will require additional time, contact the lab staff and course instructor as soon as reasonably possible; do not wait until the end of the quarter.

We value diversity and inclusion. We are committed to a climate of mutual respect and full participation. Our goal is to create learning environments that are usable, equitable, inclusive, and welcoming. If there are aspects of the instruction or design of this course that result in barriers to your inclusion, achievement, or the accurate assessment of your learning, please notify the instructor and lab staff as soon as possible.

The University of Chicago is committed to ensuring equitable access to our academic programs and services. Students with disabilities who have been approved for the use of academic accommodations by Student Disability Services (SDS) and need a reasonable accommodation(s) to participate fully in this course should follow the procedures established by SDS for using accommodations. Timely notifications are required to ensure that your accommodations can be implemented. Please meet with the course instructor to discuss your access needs in this class after you have completed the SDS procedures for requesting accommodations.

You can reach SDS through the following means:

• Phone: 773-702-6000
• Email: disabilities@uchicago.edu
• Website: https://disabilities.uchicago.edu/

Acting with academic integrity means, in brief, not submitting the statements, work, or ideas of others as one’s own. Consult with the instructor or lab staff before completing assignments if you have concerns about the correct way to reference the work of others or if you are in doubt about what constitutes academic dishonesty. More generally, please familiarize yourself with the University’s policy on academic honesty and the relevant sections of the Student Handbook. Also, see our own page regarding plagiarism and academic honesty.

Failure to maintain academic integrity on an assignment will result in a penalty befitting the violation, up to and including failing the course and further University sanctions. In the event that any concerns do arise regarding this matter, we will forward all related materials to the College Dean of Students for further review and action.

The use of AI tools (such as ChatGPT, PhoenixAI, Calude, Gemini, and others) is allowed for assignments when used in support of the course learning goals. You are not required to use AI tools, but if you choose to use them for any part of an assignment, you must include a citation or note describing the extent and details of its use. Failure to properly cite AI tools is considered a violation of the University of Chicago’s Academic Honesty and Plagiarism policy, with possible penalties ranging from loss of credit on an assignment up to referral to the Dean of Students. If you are unclear if something is an AI tool or how to cite such use, please check with the instructors.

Do not use AI tools to perform analysis tasks or compile information that you cannot independently verify. Whether AI-generated or not, when you submit an assignment with your name on it, you are responsible for that content..

• Use of AI to generate Python or LaTeX code snippets is allowed, but do so with great caution and care. You are expected to understand what AI-generated code is doing and be able to edit it if modifications are needed.
• Use of AI to seek explanation or gather information about a topic is allowed, but verify what you learn. (Lab instructors are always happy to discuss physics with you!)
• Use of AI to do in-the-moment calculations or to quickly find a literature value or physical constant is allowed, but do not cite an AI tool as your source in any formal submissions. Verify with (and cite) a primary source instead.

If the use of AI circumvents the learning process (for example, by outsourcing the analysis, critical thinking, or judgement required for a task), do not use AI for that task. Citing AI-derived work means you are not committing plagiarism… but if you do no original work, you will be given no points.

• Do not use AI tools to plagiarize; to fabricate, manipulate, or falsify data; or to generate or manipulate images.
• Do not use AI tools to generate a first draft document. The process of writing (especially at the first draft stage) is an extension of the process of thinking. We want you to think in this class, and you miss out on that opportunity by outsourcing your thinking to AI.
• Do not pass work from others through an AI tool in order to “paraphrase” or “summarize” the text for use in your submission. As discussed on the plagiarism and academic honesty page, close paraphrase is plagiarism!

This policy is not an exhaustive list of dos and don'ts. If you are ever unsure whether your AI use is appropriate or not, please speak with the instructors.

As an example of how AI use is being handled in the professional physics world, here are the current American Physical Society guidelines for journal article submission and review: https://journals.aps.org/authors/ai-based-writing-tools.