FNALLPC/machine-learning-das
Machine Learning DAS Short Exercise with CMS Open Data
This project provides tutorials for high-energy physicists to build machine learning models for analyzing data from particle collisions. It takes raw event data from experiments like CMS, and outputs classifications of particle events or jets (e.g., differentiating Higgs bosons from background noise, or W bosons from QCD jets). It's designed for physicists attending data analysis schools who want to apply modern ML techniques to their ROOT-based analyses.
Use this if you are a physicist working with particle physics data and need to learn how to apply machine learning to classify events or jets in your analyses using Python.
Not ideal if you are looking for a plug-and-play tool for general data analysis, or if you do not have a background in particle physics and Python.
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12
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16
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Mar 16, 2026
Commits (30d)
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