FNALLPC/machine-learning-hats
FNAL LPC Machine Learning HATS
This project provides hands-on tutorials for high-energy physicists working on CMS experiments to build machine learning models. You'll learn to differentiate particle events, like VBF Higgs from background, and classify jets, such as boosted W bosons from QCD, using data typically found in ROOT-based analyses. These tutorials are for experimental particle physicists and data analysts in high-energy physics who need to apply advanced machine learning techniques to their particle physics data.
Use this if you are a CMS experimentalist or data analyst who wants to integrate machine learning models like BDTs, neural networks, or more advanced techniques into your ROOT-based particle physics analysis workflows.
Not ideal if you are looking for a general machine learning course or a solution for domains outside of high-energy particle physics and CMS experiments.
Stars
18
Forks
34
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Mar 16, 2026
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/FNALLPC/machine-learning-hats"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related frameworks
arogozhnikov/hep_ml
Machine Learning for High Energy Physics.
CompPhysics/ComputationalPhysics2
Advanced course in Computational Physics, see texbook at...
DeepLearningForPhysicsResearchBook/deep-learning-physics
This project contains additional material for the textbook Deep Learning for Physics Research by...
desy-ml/cheetah
Fast and differentiable particle accelerator optics simulation for reinforcement learning and...
iml-wg/HEPML-LivingReview
Living Review of Machine Learning for Particle Physics