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AI model trained on 120M physics events improves collision data classification

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new foundation model for event classification in high-energy physics, utilizing a Graph Neural Network architecture. This model was pretrained on 120 million simulated proton-proton collision events across 12 physics processes to learn general representations of collision data. Fine-tuning the model demonstrated significant improvements in accuracy and computational efficiency for various classification tasks, including those involving new physics processes and real-world ATLAS Open Data, showcasing its generalizability. AI

IMPACT This foundation model could accelerate research in high-energy physics by improving the efficiency and accuracy of event classification.

RANK_REASON This is a research paper detailing a new model for physics analysis. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AI model trained on 120M physics events improves collision data classification

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This is a research paper detailing a new model for physics analysis. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Joshua Ho, Benjamin Ryan Roberts, Shuo Han, Haichen Wang ·

    Pretrained Event Classification Model for High Energy Physics Analysis

    arXiv:2412.10665v2 Announce Type: replace-cross Abstract: We introduce a foundation model for event classification in high-energy physics, built on a Graph Neural Network architecture and trained on 120 million simulated proton-proton collision events spanning 12 distinct physics…

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