Update based on Julie's observations

episodes/01-introduction.md

@@ -0,0 +1,68 @@
+---
+title: "Introduction"
+teaching: 30
+exercises: 60
+---
+
+:::::::::::::::::::::::::::::::::::::: questions 
+
+- Why is collaboration and knowledge sharing important in particle physics?
+- What is the significance of open data and the open science community?
+- What direction and challenges will we take in this hackathon?
+
+::::::::::::::::::::::::::::::::::::::::::::::::
+
+::::::::::::::::::::::::::::::::::::: objectives
+
+- Understand the importance of collaboration and knowledge sharing in particle physics.
+- Learn about the significance of open data and the role of the open science community.
+- Get an overview of the different lessons and challenges in the hackathon.
+
+::::::::::::::::::::::::::::::::::::::::::::::::
+
+## Welcome to the CMS Open Data Workshop & Hackathon 2024!
+
+### Importance of Collaboration and Knowledge Sharing
+
+In the field of particle physics, collaboration and knowledge sharing are crucial. The CMS Open Data Workshop & Hackathon aims to foster these values by providing a platform for participants to work together, share insights, and learn from one another. By collaborating on complex problems and sharing our findings, we can push the boundaries of what we know and achieve breakthroughs that would be impossible to accomplish alone.
+
+::::::::::::::::::::::: testimonial
+*By working together, we can leverage our collective expertise and creativity to solve complex problems and advance the field of particle physics.*
+:::::::::::::::::::::::
+
+### The Importance of Open Data and the Open Science Community
+
+Open data is a cornerstone of modern scientific research. By making data freely available to the public, we enable a broader range of scientists and enthusiasts to engage with it, leading to more robust and innovative discoveries. The open science community thrives on transparency, accessibility, and collaboration, and CMS Open Data is a perfect example of these principles in action. Through this hackathon, we aim to demonstrate the power of open data and encourage more people to contribute to the open science movement.
+
+::::::::::::::::::::::: testimonial
+*Open data allows for greater transparency and reproducibility in research, fostering innovation and enabling more people to contribute to scientific discoveries.*
+:::::::::::::::::::::::
+
+### Direction and Challenges of the Hackathon
+
+This section is tailored for our remote participants who do not have an active research task for Open Data. These activities are designed to provide a comprehensive learning experience and allow you to engage deeply with particle physics, data analysis, and machine learning from a distance.
+
+#### Key Activities
+
+1. **Particle Physics Playground**: Dive into fundamental concepts in particle physics through interactive exercises. Revisit the Particle Physics Primer pre-learning lesson to explore basic principles and engage with various scenarios to enhance your understanding.
+2. **Particle Discovery Lab**: Analyze real particle collision data from the CMS experiment using Python containers. Clone repositories, follow instructions, and perform both basic and advanced data analysis tasks to identify different particles and gain hands-on experience with real-world data.
+3. **Machine Learning 1**: Get introduced to the application of machine learning in high-energy physics (HEP). Learn initial concepts and practical applications of ML techniques in analyzing particle collision data.
+4. **Machine Learning 2**: Delve deeper into supervised and unsupervised learning methods. Follow detailed instructions to apply these techniques for classifying particle collisions, and gain practical experience in data preparation, model training, and evaluation.
+5. **Analysis Grand Challenge**: Participate in a more extensive exercise using up-to-date HEP software tools. Engage with tasks such as creating synthetic data from older CMS data, tackling generative modeling challenges, and validating complex models.
+
+
+These activities are designed to provide a rich learning experience and help you contribute meaningfully to the open science community. Enjoy exploring and collaborating with fellow participants throughout the hackathon!
+
+::::::::::::::::::::::::::::: callout
+## Challenge Yourself and Collaborate!
+
+Join us in this exciting journey of discovery and innovation. By participating in the CMS Open Data Workshop & Hackathon, you are contributing to a global community of scientists and enthusiasts working towards a common goal. Let's push the boundaries of our knowledge together!
+:::::::::::::::::::::::::::::
+
+::::::::::::::::::::::::::::::::::::: keypoints 
+
+- Collaboration and knowledge sharing are essential in advancing particle physics research.
+- Open data enables greater transparency, accessibility, and innovation in scientific research.
+- This hackathon offers a variety of lessons and challenges suitable for participants with different interests and skill levels.
+
+::::::::::::::::::::::::::::::::::::::::::::::::

episodes/01-ppp.md → episodes/02-ppp.md

@@ -60,6 +60,7 @@ Participants will be introduced to various tools and techniques used in particle
 
 ### 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.

episodes/03-pdl.md

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+---
+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?
+::::::::::::::::::::::::::::::::::::::::::::::::
+
+::::::::::::::::::::::::::::::::::::: 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.
+::::::::::::::::::::::::::::::::::::::::::::::::
+
+
+## Particle Discovery Lab
+
+The goal of this exercise is to reconstruct decays of an unknown particle X (initial state) to 2 muons (final state). Participants will display histograms for the calculated mass of particle X and learn about fitting and subtracting background distributions from data.
+
+Uncertainty propagation concepts are included at each step of the analysis. After isolating the signal distribution, participants will determine which particle they have discovered and compare its properties (mass and width) to known values.
+
+### Get Ready
+
+1. **Prepare Your Environment**:
+   - Ensure that the `my_python` container is ready. Refer to the pre-exercise instructions for Docker setup and container creation.
+
+2. **Set Up and Launch Jupyter Lab**:
+   - Execute the following commands in your terminal:
+
+     ```sh
+     docker start -i my_python
+     mkdir Particle_Discovery_Lab
+     cd Particle_Discovery_Lab
+     git clone https://github.com/DanielaMerizalde/CMS-Workshop.git
+     cd ..
+     jupyter-lab --ip=0.0.0.0 --no-browser
+     ```
+
+   - After running the last command, open the provided link in your browser to access Jupyter Lab.
+
+3. **Verify Files**:
+   - Ensure that the following files are present in the `Particle_Discovery_Lab` directory after cloning the repository:
+     - `pollsf.py`
+     - `Particle_Discovery_Lab.ipynb`
+     - `DoubleMuParked_100K.pkl`
+
+### Instructions for the Exercise
+
+1. **Launch Jupyter Lab**:
+   - In Jupyter Lab, navigate to `Particle_Discovery_Lab.ipynb` from the file menu and open it.
+
+2. **Complete the Notebook**:
+   - Follow the instructions in the notebook to complete the analysis interactively. You will perform tasks such as plotting histograms, fitting data, and analyzing uncertainties.
+
+
+::::::::::::::::::::::::::::: 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.
+
+:::::::::::::::::::::::::::::
+
+### Recommendations for Hackathon Activities
+
+Participants in the hackathon can leverage their skills and the themes explored in the Particle Discovery Lab to tackle innovative challenges and projects. Here are some suggested activities:
+
+- **Advanced Particle Identification Algorithms**: Develop and implement advanced algorithms for particle identification using collision data.
+- **Enhanced Data Visualization Tools**: Create interactive tools for exploring and analyzing CMS collision data in real-time.
+- **Integration of Machine Learning**: Apply machine learning techniques to automate data analysis and improve particle identification accuracy.
+- **Collaborative Analysis Projects**: Form teams to tackle complex analysis challenges or develop new methodologies for studying particle interactions.
+- **Educational Outreach and Visualization**: Design educational materials or demos that explain particle physics principles using CMS collision data.
+- **Open Data Innovation**: Develop tools or platforms to enhance accessibility and usability of CMS Open Data for the scientific community.
+
+These activities encourage innovation, collaboration, and exploration of particle physics concepts beyond the basic lab exercises.
+
+
+::::::::::::::::::::::::::::::::::::: keypoints
+
+Introduction to particle collision data.
+Techniques for identifying particles such as muons and electrons.
+Methods for performing both basic and advanced data analysis.
+::::::::::::::::::::::::::::::::::::::::::::::::