PHYS 434 A Au 25: Advanced Laboratory: Computational Data Analysis

Lab Sections (B143)
Tuesday (AA) 1:30-4:20
TA: Linda Zhenyu Jin (lindajin@uw.edu)

Course Overview

This repository contains laboratory notebook templates and datasets as well as final projects I designed for Physics 434 "Advanced Data Analysis Techniques for Large Datasets."

Lab/Project	Title	Dataset	Key Statistical Concepts	Methods & Techniques
Lab 1	HTRU2 Pulsar Analysis	Pulsar survey data	Probability distributions, Bayes' theorem, conditional probability	Histogram analysis, Bayesian updating, statistical inference
Lab 2	Variable Transformation	Synthetic distributions	Variable transformation, PDF theory, distribution validation	Analytic PDF derivation, Monte Carlo sampling, distribution comparison
Lab 3	Monte Carlo & Ising Model	Simulated physical systems	Monte Carlo integration, rejection sampling, phase transitions, statistical mechanics	Rejection method, Metropolis algorithm, numerical integration, uncertainty estimation
Lab 4	Maximum Likelihood	Particle mass distribution	maximum likelihod estimation (MLE), parameter estimation, Cauchy distribution, optimization	Negative log-likelihood minimization, chi-squared minimization, binned/unbinned fits, grid search
Lab 5	LHC Higgs Analysis	LHC jet data	Statistical significance, hypothesis testing, signal vs background, cut optimization	significance (SNR) optimization, confidence levels, event selection
Lab 6	Bayesian Regression	APOGEE stellar spectra	Bayesian inference, posterior distributions, heteroscedastic noise, uncertainty quantification, error propagation	Bayesian linear regression, MLE vs Bayesian comparison, predictive distributions, coverage testing, prior selection
Final 1	EHT Black Hole Imaging	M87 & 3C279 radio interferometry	Radio interferometry, visibility analysis, calibration, time-domain statistics, systematic errors	Chi-squared tests, variability analysis, power-law fitting, goodness-of-fit model selection, confidence intervals
Final 2	CAMELS Cosmological simulations	CMD maps	parameter space exploration, data interpolation	MLE, chi-squared minimization, MCMC sampling, likelihood surface mapping, Bayesian parameter estimation,importance sampling

Getting Started

1. Click the "Fork" button in the top-right corner of the repository page

2. Choose where to fork: Select your personal account or organization Optionally change the repository name Click "Create fork"

3. Start editing

Option 1: Local Setup (VS Code)

1. Clone this repository:

   git clone https://github.com/Klinjin/au25_phys434_data_analysis_lab.git
   cd au25_phys434_data_analysis_lab

2. Set up conda environment:

   First, install Conda: https://conda.io/projects/conda/en/latest/user-guide/install/index.html

   conda create --name phys434 python=3.12.8
   conda activate phys434
   pip install -r requirements.txt

   Optional: To deactivate and remove the environment later:

   conda deactivate
   conda env remove --name phys434

3. Open in VS Code:

   • Install the Python and Jupyter extensions in VS Code
   • Open the repository folder in VS Code
   • Click on any .ipynb file to start working with notebooks

Option 2: Local Setup (Browser Terminal)

1. Clone and navigate to repository:

   git clone https://github.com/YOUR_USERNAME/au25_phys434_data_analysis_lab.git
   cd au25_phys434_data_analysis_lab

2. Install dependencies and start Jupyter:

   pip install pandas matplotlib numpy scipy jupyter
   jupyter notebook

3. Access notebooks:

   • Your browser will open automatically
   • Navigate to the notebook files (.ipynb) to start working

Option 3: Google Colab

1. Open in Colab:

   Lab 1
   

2. Mount Google Drive (run this in the first cell):

   Important: Google Colab's memory is ephemeral - your work will be lost when the session ends. Either download your file in the end or mount your Google Drive to save your edits and outputs permanently.

   from google.colab import drive
   drive.mount('/content/drive')
   %cd /content/drive/MyDrive/

Submitting your work

1. Create your branch:

   git checkout -b [YOURBRANCH]

2. Commit your changes:

   git add .
   git commit -m "Complete Lab 1 exercises"
   git push origin [YOURBRANCH]

3. Submit Pull Request: Go to your own forked repository on Github "Pull requests"--"New pull request" In the description write down your full name.

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Last updated: September 2025