Research conceptCERN · High-energy physics
Machine Learning for Collider Events
A proposed analysis of LHC proton-collision events for separating Standard Model backgrounds from rare signatures associated with Higgs processes or dark-matter searches.
Project record
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Methods and evidence
Machine Learning for Collider Events
A proposed analysis of LHC proton-collision events for separating Standard Model backgrounds from rare signatures associated with Higgs processes or dark-matter searches.
- Data
- Open CERN/LHC collision records paired with Monte Carlo simulation.
- Signals
- Missing transverse energy, mono-jet, and mono-X topologies against QCD and other backgrounds.
- Evaluation
- Classifier quality should be paired with systematic uncertainties and physics significance, not accuracy alone.
Evidence boundary: the file defines the scientific problem and evaluation direction but does not contain an executed analysis or result.