#AAAI2026 invited talk: machine learning for particle physics

2026-04-01 · Source: ΑΙhub · Field: Science & Research — Physical Sciences & Chemistry, Mathematics & Computational Sciences · Depth: Intermediate, short

Summary

Daniel Whiteson, a particle physicist at CERN's Large Hadron Collider (LHC), delivered an invited talk at AAAI-26, discussing the application of machine learning in analyzing high-energy proton-proton collisions at 13 TeV. Particle physicists have utilized machine learning since the 1990s, initially for dimensionality reduction and later, in 2012, for the Higgs boson discovery using deep neural networks. Currently, ML is pervasive for data classification, simulated data generation, and experiment optimization. Whiteson highlighted the "particle tracking problem," where physicists reconstruct particle paths from detector traces. Current algorithms assume particles originate at collision points and follow helical paths through magnetic fields. Whiteson's research focuses on using graph neural networks to analyze non-helical paths, potentially uncovering new, un-theorized particles like theoretical "quirks" that exhibit oscillating trajectories.

Key takeaway

For AI Scientists and Research Scientists working on complex data reconstruction, consider how current algorithmic assumptions might be limiting your discovery potential. Daniel Whiteson's work demonstrates that employing graph neural networks can help identify non-standard patterns, such as non-helical particle paths, that are currently overlooked. Evaluate your data processing pipelines for embedded assumptions and explore ML techniques to generalize beyond them, potentially revealing novel phenomena.

Key insights

Machine learning, especially graph neural networks, can overcome limiting assumptions in particle tracking to discover new physics.

Principles

• Assumptions limit discovery potential.
• Separate particle finding from path fitting.
• Graph networks excel with structured data.

Method

A graph neural network maps detector hits to a latent space, grouping hits from the same particle track. The network is trained on specific path types (e.g., helical, oscillating) to fit trajectories.

In practice

• Apply GNNs to reconstruct complex trajectories.
• Challenge existing data processing assumptions.
• Train models on theoretical particle paths.

Topics

• Machine Learning
• Particle Physics
• Large Hadron Collider
• Graph Neural Networks
• Particle Tracking

Best for: AI Scientist, Research Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by ΑΙhub.