Multi Jet QCD Event

Multi-jet QCD events, arising from the strong interaction of quarks and gluons, are a significant background in high-energy physics experiments, often obscuring signals from new physics. Current research focuses on developing advanced machine learning techniques, such as graph neural networks, normalizing flows, and autoencoders, to efficiently and accurately identify and classify these events, improving signal-to-noise ratios. These efforts leverage sophisticated algorithms to reconstruct particle jets and employ innovative loss functions to address challenges like data sparsity. Improved jet identification significantly enhances the sensitivity of searches for new physics phenomena at colliders like the LHC.