探寻未见之物：半可见喷注标记的深度学习分析
Hunting the Unseen: Deep Learning Analysis for Semi-Visible Jet Tagging
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中文总结 AI 辅助
本研究利用深度学习结合全局运动学与喷注子结构特征标记半可见喷注，发现全局运动学最优，融合互补信息可提升性能，为LHC搜索提供基准。
中文摘要 AI 辅助
半可见喷注（SVJ）构成了强相互作用暗扇区的独特对撞机特征，其中包含暗物质候选粒子，喷注内同时含有可见的标准模型物体和不可见的暗强子，从而产生关联的喷注活动和缺失横动量。在本工作中，我们研究了通过重Z'媒介产生的半可见喷注，并在所谓的隐藏山谷模型背景下，对跨越不同媒介质量和暗扇区参数的一组代表性基准场景进行了研究。为了表征信号，我们将全局事件运动学与喷注子结构观测量相结合，包括主Lund喷注平面（LJP）、两点能量关联、角性以及带电强子多重性。这些表示被用于训练五个用于半可见喷注与标准喷注区分的深度学习分类器：一个在LJP图像上运行的视觉变换器，一个基于层次聚类树的JetLOV网络，一个使用高层观测量的多层感知器，以及两个将基于图像或层次结构的辐射模式表示与高层喷注观测量相结合的多模态网络。这使得全局运动学、辐射模式和喷注聚类结构得以直接结合。我们发现全局运动学观测量优于LJP和层次喷注表示，而后者比LJP图像提供更强的区分能力。将这些互补表示与全局运动学相结合可获得最佳整体性能。更广泛地说，这项研究表明，释放半可见喷注的全部发现潜力将受益于超越全局运动学，利用其内部结构中编码的丰富信息，为大型强子对撞机的未来搜索提供了基准。
英文摘要
Semi-Visible Jets (SVJs) constitute a distinctive collider signature of strongly interacting dark sectors, embedding Dark Matter candidates, wherein jets contain both visible Standard Model objects and invisible dark hadrons, giving rise to correlated jet activity and missing transverse momentum. In this work, we investigate SVJs produced through a heavy Z' mediator and perform an study over a representative set of benchmark scenarios spanning different mediator masses and dark sector parameters in the context of so-called Hidden Valley Models. To characterise the signal, we combine global event kinematics with jet substructure observables, including the primary Lund Jet Plane (LJP), the two-point energy correlation, angularity, and charged hadron multiplicity. These representations are used to train five Deep Learning classifiers for SVJ vs standard jet discrimination: a Vision Transformer operating on LJP images, a JetLOV network based on a hierarchical clustering tree, a Multi-Layer Perceptron using high level observables, and two multimodal networks that combine the image-based or hierarchical representations of the radiation pattern with the high jet-level observables. This enables a direct combination of global kinematics, radiation patterns, and jet clustering structure. We find that global kinematic observables outperform the LJP and hierarchical jet representations, with the latter providing stronger discrimination than LJP images. Combining these complementary representations with global kinematics yields the best overall performance. More broadly, this study shows that unlocking the full discovery potential of SVJs would benefit from going beyond global kinematics to exploit the rich information encoded in their internal structure, providing a benchmark for future searches at the Large Hadron Collider.
发表机构
- University of Southampton（南安普顿大学）
- Rutherford Appleton Laboratory（卢瑟福·阿普尔顿实验室）
- KIAS（韩国高等科学研究院）
- Uppsala University（乌普萨拉大学）
- KEK（高能加速器研究机构）
- The Graduate University of Advanced Studies (Sokendai)（综合研究大学院大学（索肯代））
- Kavli IPMU (WPI), University of Tokyo（东京大学木户宇宙物质研究所（世界顶级研究中心））
- University of Manchester（曼彻斯特大学）
- CERN（欧洲核子研究组织）
机构由 AI 辅助整理，请以论文原文为准。
