通过特征导向预训练加速共振态搜索
Accelerating Resonance Searches via Signature-Oriented Pre-training
AI总结：
针对LHC重共振态搜索中现有深度学习方法仅覆盖特定末态的局限，提出基于JetClass-II预训练的Sophon方法，可覆盖大量增强末态，提升模型特异性与模型无关搜索性能，加速共振态探索。
摘要：
寻找超出标准模型（BSM）的重共振态是大型强子对撞机（LHC）的核心目标之一。尽管近期将先进深度神经网络用于增强型喷注标记，显著提升了定向搜索的灵敏度，但该方法仅局限于特定末态，导致大量潜在的BSM相空间未得到充分探索。我们提出了一种全新的实验方法——面向特征的重共振态观测预训练（Sophon），该方法利用深度学习覆盖大量增强型末态。Sophon模型在全面的JetClass-II数据集上进行预训练，学习复杂的喷注特征，确保各类喷注标记判别器的最优构建，并实现高性能的迁移学习能力。我们表明，该方法不仅能将广泛的模型特异性搜索推至其灵敏度前沿，还能大幅提升模型无关方法的性能，从广义上加速LHC的共振态搜索。
英文摘要：
The search for heavy resonances beyond the Standard Model (BSM) is a key objective at the LHC. While the recent use of advanced deep neural networks for boosted-jet tagging significantly enhances the sensitivity of dedicated searches, it is limited to specific final states, leaving vast potential BSM phase space underexplored. We introduce a novel experimental method, Signature-Oriented Pre-training for Heavy-resonance ObservatioN (Sophon), which leverages deep learning to cover an extensive number of boosted final states. Pre-trained on the comprehensive JetClass-II dataset, the Sophon model learns intricate jet signatures, ensuring the optimal constructions of various jet tagging discriminates and enabling high-performance transfer learning capabilities. We show that the method can not only push widespread model-specific searches to their sensitivity frontier, but also greatly improve model-agnostic approaches, accelerating LHC resonance searches in a broad sense.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
