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Modern Machine Learning and Particle Physics
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Over the past five years, modern machine learning has been quietly revolutionizing particle physics. Old methodology is being outdated and entirely new ways of thinking about data are becoming commonplace. This article will review some aspects of the natural synergy between modern machine learning and particle physics, focusing on applications at the Large Hadron Collider. A sampling of examples is given, from signal/background discrimination tasks using supervised learning to direct data-driven approaches. Some comments on persistent challenges and possible future directions for the field are included at the end.
Forward citations
Cited by 5 Pith papers
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Simplex Demixing: Disentangling Multiple Light-Flavor Jets at Colliders
Simplex demixing recovers T mutually irreducible jet-flavor topics from M mixed samples via the (T−1)-simplex geometry of a multi-category classifier, demonstrated on Pythia dijets.
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Explainable AI-assisted Optimization for Feynman Integral Reduction
FunSearch discovered a simple priority function for ordering IBP seeding integrals, reducing the number needed for multi-loop Feynman integral reductions by factors up to 3058.
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A Step Toward Interpretability: Smearing the Likelihood
Smearing the likelihood over an energy metric reveals the physical scales used by a jet classifier, and the needed smearing radius follows a power-law scaling with dataset size.
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Shedding Light on Dark Matter at the LHC with Machine Learning
A machine-learned LHC analysis projects 5-sigma sensitivity to singlino-dominated NMSSM dark matter via radiative higgsino decays to photons, covering higgsino masses up to 225 GeV.
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Jet Image Tagging Using Deep Learning: An Ensemble Model
An ensemble of ResNet50 and InceptionV3 convolutional networks classifies JetNet jet images with about 75% multi-class accuracy, slightly better than each network alone.
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