arogozhnikov/hep_ml
Machine Learning for High Energy Physics.
This project offers specialized machine learning tools to help high energy physicists analyze experimental data. It takes raw event data from particle collisions and applies classification and reweighting techniques to help identify significant patterns and remove unwanted biases. Particle physicists and researchers working with large datasets from accelerators would use this to improve their data analysis workflows.
197 stars. Available on PyPI.
Use this if you need to build classifiers that are robust against biases from specific variables like particle mass, or if you need to reweight multi-dimensional distributions in high energy physics experiments.
Not ideal if your work is outside of high energy physics or you need general-purpose machine learning tools not specific to particle physics data analysis.
Stars
197
Forks
68
Language
Jupyter Notebook
License
—
Category
Last pushed
Dec 01, 2025
Commits (30d)
0
Dependencies
4
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