machine-learning-hats and machine-learning-das
About machine-learning-hats
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.
About machine-learning-das
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.
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