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English(EN) Cross-Domain Transfer with Particle Physics Foundation Models: From Jets to Neutrino Interactions

粒子物理基础模型展现跨域迁移能力

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了用于粒子物理的基础模型 OmniLearned 和 ParticleViT，它们展现了跨域迁移能力。这些模型在多样化的碰撞数据上进行了预训练，与从头开始训练的模型相比，在中微子相互作用任务上的表现有所提高。值得注意的是，粒子级预训练相比于 BERT 等不相关的文本预训练具有显著优势，这表明这些基础模型获得了用于粒子物理中探测器无关推理的可泛化归纳偏差。 AI

影响 展示了基础模型在粒子物理等专业科学领域加速研究和提高灵敏度的潜力。

排序理由 这是一篇详细介绍新模型开发和评估的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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

粒子物理基础模型展现跨域迁移能力

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这是一篇详细介绍新模型开发和评估的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Gregor Krzmanc, Vinicius Mikuni, Benjamin Nachman, Callum Wilkinson ·

    基于粒子物理基础模型的跨域迁移：从喷流到中微子相互作用

    arXiv:2604.12364v2 Announce Type: replace-cross Abstract: Future AI-based studies in particle physics will likely start from a foundation model to accelerate training and enhance sensitivity. As a step toward a general-purpose foundation model for particle physics, we investigate…

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