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

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

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

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

在 arXiv cs.LG 阅读 →

• arXiv
• ATLAS Open Data
• Graph Neural Network

• 论文
• 其他

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →