Particle Physics Data Analysis with Python

Program: PYAR/CrEST Outreach Institution: UC Santa Cruz Role: Curriculum Developer & Instructor

Overview

A series of Jupyter notebooks designed to introduce high school and early undergraduate students to particle physics data analysis. The curriculum guides students from Python basics through analyzing real CERN Open Data to discover the Z boson.

Developed as part of the PYAR (Python and Research) and CrEST (Creating Equity in STEM) programs at UC Santa Cruz, which aim to increase diversity and access to physics research for underrepresented students.

Curriculum

Notebook	Topics
1. Python Intro	Variables, lists, loops, functions, modules
2. Plotting	NumPy, Matplotlib, histograms, scatter plots
3. Coordinate Systems	Cartesian, polar, spherical, cylindrical transforms
4. Particle Physics Primer	Four-vectors, collider coordinates, mass reconstruction
5. Data Extraction	Pandas, CSV parsing, CERN Open Data
6. Invariant Mass Plotting	Full analysis pipeline, Z boson identification

Physics Concepts

Collider Coordinates

Students learn the coordinate system used at particle colliders:

Transverse momentum (\(p_T\)): Momentum perpendicular to beam
Pseudorapidity (\(\eta\)): Related to polar angle, \(\eta = -\ln\tan(\theta/2)\)
Azimuthal angle (\(\phi\)): Angle in the transverse plane

Invariant Mass

The key analysis technique - reconstructing parent particle mass from decay products:

\[ m = \sqrt{(E_1 + E_2)^2 - |\vec{p}_1 + \vec{p}_2|^2} \]

Students apply this to identify the Z boson (~91 GeV) from its decays to lepton pairs.

Data

Real collision data from the CERN Open Data Portal:

Zee.csv - Z → e⁺e⁻ (electron-positron pairs)
Zmumu.csv - Z → μ⁺μ⁻ (muon-antimuon pairs)

Acknowledgments

This curriculum was developed through the PYAR and CrEST programs at UC Santa Cruz. These programs provide research opportunities and mentorship to students from underrepresented backgrounds in physics and STEM fields.