# Lectures for PHY 451 Advanced Lab

Based on Teaching Students How to Model, I am revising the lectures for the Advanced Lab class (PHY 451).

Day 1:

1. Introduce ourselves & fill out background material (year, classes & labs taken)
2. Syllabus (timeline, grading policy, expectations)
3. Introduction to Learning Goals & Modeling Framework
4. Logbook (which kind, what to write)
5. Description of experiments
6. Choose Lab partners
7. List experiment preferences
8. Python tutorial (installation, read data file, plot data file, fit data to parabola) Lectures 1 & 2: Modeling Framework

1. Learning Goals
1. Designing & Troubleshooting Experiments
2. Technical Lab Skills (Computer DAQ systems, Test & Measurement Equipment)
3. Communication (Argumentation, PRL-style paper, APS-style talk)
4. Modeling Framework
2. Modeling Framework
1. What is modeling? A abstract representation of a real physical system that is simplified, is predictive, and has specified limits to its applicability.
2. Outline of framework
1. Two models are needed (measurement system and physical system)
2. Iterative, but needs a starting point (Prelab writeup)
3. Making comparisons (Data reduction & Uncertainty analysis)
3. Prelab writeup
1. What is being measured?
2. Why is it being measured?
3. How is it being measured?
4. What steps are required for the measurement?
5. Initial model for physical system
6. Initial model for measurement system
1. perfectly linear
2. infinite dynamic range
3. infinite range
4. zero intrinsic noise (infinite precision)
5. infinite resolution
6. instantaneous response
7. zero offset (perfectly accurate)
4. Example: Measuring voltages in a voltage divider
1. Initial model for physical system: Ohm’s Law
2. Initial model for measurement system: Galvanometer
3. Make measurements with DMM and Oscilloscope
4. Comparison
5. Refine Model

Lectures 3, 4, 5: Data Analysis Potpourri

• Statistical vs. Systematic
• Precision vs. Accuracy
• Gaussian statistical
• Poisson statistical
• Uncertainty propagation
• Examples of estimating the statistical uncertainty
• Systematics – incomplete model
• Curve fitting – (least squares)
• Anscombe’s quartet

Lecture 6: Estimating & Order of Magnitude Physics

Lecture 7,8,9: Communication (Argumentation, PRL-style paper, APS-style talk)

• data visualization
• model visualization
• citations
• components of a paper
• components of a talk
• Calling Bullshit
• types of fallacies
• McGuire Quartet
• case studies

Lecture 10: Two-level systems (optional)

• energy levels of system
• energy of photon
• absorption
• spontaneous emission
• stimulated emission
• linewidth or energy resolution
• conservation of energy
• conservation of angular momentum