Update episode content

episodes/01-ppp.md

@@ -22,15 +22,15 @@ exercises: 0
 
 ## Particle Physics Playground
 
-This lesson offers a variety of exercises designed to help undergraduates understand fundamental concepts in particle physics through practical analysis of CMS Open Data.
+This activity offers a variety of exercises designed to help undergraduates understand fundamental concepts in particle physics through practical analysis of CMS Open Data.
 
 ### Overview
 
 The Particle Physics Playground provides an engaging and interactive way for participants to delve into the core principles of particle physics. By working with real CMS Open Data, students will enhance their theoretical knowledge through hands-on experience.
 
-### Pre-learning Lesson
+## Pre-learning Lesson
 
-Before diving into the Particle Physics Playground, participants are encouraged to review the [Particle Physics Primer pre-learning lesson](https://cms-opendata-workshop.github.io/workshop2024-lesson-particle-physics-primer/instructor/index.html). This foundational lesson will prepare you for the exercises ahead.
+Participants are encouraged to review the [Particle Physics Primer pre-learning lesson](https://cms-opendata-workshop.github.io/workshop2024-lesson-particle-physics-primer/instructor/index.html). This foundational lesson is equipped with lectures and exercises covering the following topics:
 
 ### Fundamental Concepts
 
@@ -42,12 +42,32 @@ One of the main tasks in this lesson is to analyze particle decay patterns. By s
 
 ### Practical Analysis Tools
 
-Participants will be introduced to various tools and techniques used in particle physics analysis. This includes the use of ROOT for data analysis, as well as other software and frameworks commonly employed in the field. The goal is to equip students with the practical skills needed to conduct their own analyses and contribute to ongoing research.
+Participants will be introduced to various tools and techniques used in particle physics analysis. This includes the use of Python and Jupyter notebooks hosted on Google Colab for data analysis, as well as simplified data formats provided by the Particle Physics Playground website. The goal is to equip students with the practical skills needed to conduct their own analyses and contribute to ongoing research.
+
+## Recommendations
+
+### Activity Suggestions
+
+1. **Watch the Lectures**: Start by watching the first three lectures by Dr. Allison Hall to build a strong theoretical foundation.
+2. **Try the Exercises**: Attempt the associated exercises to test your understanding and apply what you've learned.
+3. **Explore the Toy Analyses**: Engage with the toy analyses to gain hands-on experience with real CMS data, focusing on:
+   - Working with Colab and understanding data formats.
+   - Learning about particle lifetimes and identifying detectable particles.
+   - Calculating masses using 4-vectors and creating histograms.
+   - Discovering new particles by analyzing decay products.
+
+4. **Discuss and Collaborate**: Use discussion forums or collaborative platforms to share your findings, ask questions, and work with peers.
+
+### Additional Resources
+
+- **Particle Physics Primer Videos**: Watch public-oriented videos for a broad overview.
+- **Standard Model References**: Review materials on the Standard Model and its historical development.
+- **Advanced Lectures**: For those interested, watch the remaining lectures covering neutrino physics and dark matter.
 
 ::::::::::::::::::::::::::::: callout
 ## You Have Choices!
 
-While ROOT and C++ are essential for early-stage analysis of CMS Open Data in the AOD (Run 1) or MiniAOD (Run 2) formats, participants can use other tools and file formats for downstream analysis or for analyzing Run 2 NanoAOD files. Feel free to choose the tools that best suit your needs and preferences.
+While Python and Jupyter notebooks are the primary tools for this activity, feel free to explore other tools and file formats that suit your needs. The goal is to learn and apply particle physics analysis techniques in a way that works best for you.
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episodes/02-pdl.md

@@ -1,61 +1,68 @@
----
 title: "Particle Discovery Lab"
 teaching: 30
 exercises: 60
----
 
-:::::::::::::::::::::::::::::::::::::: questions 
-
-- How can we identify different particles in collision data?
-- What are the characteristics of muons in the dataset?
-- How do we perform basic and advanced data analysis in particle physics?
+:::::::::::::::::::::::::::::::::::::: questions
 
+How can we identify different particles in collision data?
+What are the characteristics of muons in the dataset?
+How do we perform basic and advanced data analysis in particle physics?
 ::::::::::::::::::::::::::::::::::::::::::::::::
 
 ::::::::::::::::::::::::::::::::::::: objectives
 
-- Reconstruct decays of an unknown particle X to 2 muons.
-- Use histograms to display the calculated mass of particle X.
-- Learn to fit and subtract background contributions from data.
-- Understand uncertainty propagation throughout the analysis.
-- Identify the discovered particle and compare its properties to known values.
-
+Reconstruct decays of an unknown particle X to 2 muons.
+Use histograms to display the calculated mass of particle X.
+Learn to fit and subtract background contributions from data.
+Understand uncertainty propagation throughout the analysis.
+Identify the discovered particle and compare its properties to known values.
 ::::::::::::::::::::::::::::::::::::::::::::::::
 
-## Particle Discovery Lab (for students!)
+## Particle Discovery Lab
 
 Participants will analyze real particle collision data from the CMS experiment. They will identify different particles, such as muons and electrons, by examining the data's characteristics. This exercise helps students understand how physicists discover and study fundamental particles.
 
-### Overview
-
+## Overview
 The Particle Discovery Lab is designed to introduce participants to the fascinating world of particle physics by working with actual data from the CMS experiment. This hands-on experience will provide valuable insights into the process of particle identification and the analysis techniques used by physicists.
 
 ### Identifying Particles
-
 Participants will learn to identify different particles by analyzing their collision data. Key characteristics such as energy, momentum, and decay patterns will be examined to distinguish between various particles. The focus will be on identifying muons and electrons, which are fundamental components in many particle physics studies.
 
 ### Basic and Advanced Data Analysis
-
 The lab will guide participants through both basic and advanced data analysis tasks. Initially, they will perform simple tasks such as plotting histograms and calculating basic statistics. As they progress, more advanced techniques will be introduced, including fitting data to theoretical models and performing complex statistical analyses.
 
-### Instructions for the Exercise
-
+## Instructions for the Exercise
 To get started with the Particle Discovery Lab, follow these steps:
 
-1. **Set Up the Python Container**: Ensure you are working within the provided Python container environment.
-2. **Clone the Repository**: Open a terminal in the Python container and run the following command to clone the repository:
-   ```bash
-   git clone https://github.com/bethel-physics/ParticleDiscoveryLab
-3. **Follow the Instructions**: Navigate to the cloned repository directory and follow the instructions provided in either the Python script or the Jupyter notebook to complete the exercise.
-
-### Visualize with CMS Spy WebGL
-To enhance your understanding and visualization of the particle collision events, use the [CMS Spy WebGL visualizer](https://opendata.cern.ch/visualise/events/cms#). This tool provides a 3D visualization of the CMS collision data, allowing you to better grasp the spatial distribution and interactions of particles.
+1. Set Up the Python Container: Ensure you are working within the provided Python container environment.
+2. Clone the Repository: Open a terminal in the Python container and run the following command to clone the repository:
+```bash
+git clone -b instructors https://github.com/bethel-physics/ParticleDiscoveryLab.git
+cd ParticleDiscoveryLab/PythonAnalysis
+```
+3. Run the Analysis: Navigate to the cloned repository directory and follow the instructions provided in either the Python script or the Jupyter notebook to complete the exercise.
+- Script: Run the following command:
+```bash
+python MuonAnalysis_student.py
+```
+- Jupyter Notebook:
+
+- Start Jupyter Lab:
+
+```bash
+jupyter lab
+```
+- Open MuonAnalysis_student.ipynb and follow the instructions.
+
+## Visualize with CMS Spy WebGL
+To enhance your understanding and visualization of the particle collision events, use the CMS Spy WebGL visualizer. This tool provides a 3D visualization of the CMS collision data, allowing you to better grasp the spatial distribution and interactions of particles.
 
 ::::::::::::::::::::::::::::: callout
 
 You Have Choices!
 
 While ROOT and C++ are essential for early-stage analysis of CMS Open Data in the AOD (Run 1) or MiniAOD (Run 2) formats, participants can use other tools and file formats for downstream analysis or for analyzing Run 2 NanoAOD files. Feel free to choose the tools that best suit your needs and preferences.
+
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