Machine learning for the LHC physics program: a 2025-2026 stocktake

arXiv:2609.32874 · hep-ph, cs.LG, hep-ex · Submitted 2026-09-26 · Read on arXiv

hep-ph, cs.LG, hep-ex

Submitted: 2026-09-26

Updated: 2026-09-26

Terminology

• Foundation Model — 16× in this paper · explained in 3 episodes
• AI Agents — 14× in this paper · explained in 2 episodes
• Transformer — 11× in this paper · explained in 2 episodes
• Calibration — 10× in this paper · explained in 7 episodes
• Foundation Models — 10× in this paper · explained in 7 episodes
• Agentic AI — 7× in this paper · explained in 1 episode
• Simulation-based Inference — 6× in this paper · explained in 2 episodes
• Model-Agnostic — 4× in this paper · explained in 2 episodes
• Benchmark — 3× in this paper · explained in 2 episodes
• Diffusion Model — 3× in this paper · explained in 8 episodes
• Interpretability — 3× in this paper · explained in 15 episodes
• Normalizing Flow — 3× in this paper · explained in 3 episodes
• Normalizing Flows — 3× in this paper · explained in 4 episodes
• Verification — 3× in this paper · explained in 2 episodes
• Diffusion Models — 2× in this paper · explained in 6 episodes
• Flow Matching — 2× in this paper · explained in 8 episodes
• Hallucination — 2× in this paper · explained in 4 episodes
• Large Hadron Collider — 2× in this paper
• Latent Space — 2× in this paper · explained in 7 episodes
• Scaling Laws — 2× in this paper · explained in 4 episodes
• Verifier — 2× in this paper · explained in 2 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Adaptive Response — 1× in this paper · explained in 1 episode
• Agentic AI systems — 1× in this paper · explained in 1 episode
• Artificial Intelligence (AI) — 1× in this paper · explained in 2 episodes
• Commentary — 1× in this paper · explained in 2 episodes
• Department of Energy — 1× in this paper
• Empirical Coverage — 1× in this paper · explained in 2 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• National Science Foundation — 1× in this paper
• Provenance — 1× in this paper · explained in 4 episodes
• Pruning — 1× in this paper · explained in 4 episodes
• Uncertainty Quantification — 1× in this paper · explained in 8 episodes

Sources

• Descending into the Modular Bootstrap
• The Future of Artificial Intelligence and the Mathematical and Physical Sciences (AI+MPS)
• OmniJet-$\alpha$: The first cross-task foundation model for particle physics
• Solving Key Challenges in Collider Physics with Foundation Models
• The Landscape of Unfolding with Machine Learning
• CaloDREAM -- Detector Response Emulation via Attentive flow Matching
• A simultaneous unbinned differential cross section measurement of twenty-four $Z$+jets kinematic observables with the ATLAS detector
• CaloChallenge 2022: A Community Challenge for Fast Calorimeter Simulation
• A Lorentz-Equivariant Transformer for All of the LHC
• Model-agnostic search for dijet resonances with anomalous jet substructure in proton-proton collisions at $\sqrt{s}$ = 13 TeV
• Search for new physics in final states with semi-visible jets or anomalous signatures using the ATLAS detector
• Resummation of the C-Parameter Sudakov Shoulder Using Effective Field Theory
• A Living Review of Machine Learning for Particle Physics
• Unbinned inclusive cross-section measurements with machine-learned systematic uncertainties
• A continuous calibration of the ATLAS flavour-tagging classifiers via optimal transportation maps
• Sampling NNLO QCD phase space with normalizing flows
• Transforming jet flavour tagging at ATLAS
• Tensor Network for Anomaly Detection in the Latent Space of Proton Collision Events at the LHC
• Frequentist Uncertainties on Neural Density Ratios with wifi Ensembles
• Review of Machine Learning for Real-Time Analysis at the Large Hadron Collider experiments ALICE, ATLAS, CMS and LHCb

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