AI 中文总结
研究利用弱监督机器学习进行包括希格斯玻色子在内的新物理特征异常检测搜索。核心方法是扩展HAXAD方法，引入新嵌入策略、开发推理框架并扩展信号模型集。主要贡献是提高信号灵敏度，完善统计机制，增强HAXAD作为可行搜索策略的理由。
AI 中文摘要
希格斯玻色子因其与质量的普遍耦合，为超出标准模型的领域提供了广泛适用的通道，是对撞机实验中异常检测的自然切入点。希格斯与X异常检测（HAXAD）策略通过结合基于机器学习的特征嵌入、背景估计和弱监督分类，提供了一种原则性方法来搜索与希格斯玻色子相关的异常。本工作将先前的HAXAD方法扩展到适用于对撞机记录数据所需的成熟度水平。主要新增内容是引入并比较了两种新的嵌入策略，这反过来又塑造了背景估计和分类。此外，还开发了一个新的推理框架，得出与信号无关和特定于信号的截面限制，从而完善了基于HAXAD的未来异常检测分析所需的统计机制。所研究的信号模型集也显著扩展，能够在更广泛的相空间上评估灵敏度。相对于原始方法，该方法的改进提高了信号灵敏度，与基于相同终态的基于切割的搜索示例进行基准测试时，HAXAD在各种考虑的信号模型中匹配或超过了最佳的基于切割的单个限制。这些进展强化了HAXAD作为一种可行且引人注目的基于异常检测的搜索策略在对撞机上具有新发现潜力的理由。
英文摘要
The Higgs boson, with its universal coupling to mass, provides a broadly applicable portal to sectors beyond the Standard Model and is therefore a natural anchor for anomaly detection (AD) at collider experiments. The Higgs And X Anomaly Detection (HAXAD) strategy offers a principled approach to searching for such anomalies occurring in association with a Higgs boson by combining machine-learning-based feature embedding, background estimation, and weakly supervised classification. This work extends the previous HAXAD approach towards the level of maturity required for application to recorded collider data. A major addition is the introduction and comparison of two new embedding strategies, which in turn shape the background estimation and classification. In addition, a new inference framework is developed, yielding signal-agnostic and signal-specific cross section limits and thereby completing the statistical machinery needed for future AD analyses built on HAXAD. The set of investigated signal models is also significantly expanded, allowing for the evaluation of sensitivity on a much broader phase space. Improvements to the method increase signal sensitivity with respect to the original method, and when benchmarked against an example cut-based search on the same final state, HAXAD matches or exceeds the best individual cut-based limits for a wide variety of considered signal models. These developments strengthen the case for HAXAD as a viable and compelling AD-based search strategy with novel discovery potential at colliders.
Comments27 pages, 10 figures
