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arXiv 1707.07084hep-phhep-ex
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在LHC上发现异常目标的框架

A Framework for Finding Anomalous Objects at the LHC

Amit Chakraborty, Abhishek M. Iyer, Tuhin S. Roy

备注：
32 pages, 5 tables and 12 figures; v2: references added; v3: Practical guideline given for implementation at the LHC, comments added on the possibility of inclusion of Muons and b-jets in the framework. Accepted for publication in Nuclear Physics B; v4: Title fixed from v3 to match journal version, funding information updated
发表信息：
Nucl.Phys. B932 (2018) 439-470
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摘要：

在大型强子对撞机上寻找新的物理事件主要依赖于这样一个假设，即这些事件的特征是标准重构的物体，如孤立光子、轻子和由QCD部分子引发的喷流。虽然这种策略适用于标准模型场景之外的绝大多数物理学，但也有大量新物理学产生异常物体的例子（例如准直和同等能量的粒子，由于长寿命粒子等而引起的衰变）。在探测器中，它不能被归类为任何标准对象。已经提出了各种方法和搜索策略，每种方法和搜索策略都针对特定的模型、拓扑结构和模型参数进行了训练和优化。此外，由于LHC一直排除所有预期的新物理候选者，因此对能够发现意外的通用方法/工具的需求不能低估。在本文中，我们提出了一个这样的方法，依赖于哲学，所有的异常对象是$\it{not}$标准对象。我们认为，异常发现器只是一个否决权的集合，它消除了所有标准对象，直到达到预定的接受率。任何包含至少一个异常物体的事件（通过所有这些否决）都可以被确定为新物理学的候选者。随后的离线分析可以确定异常物体以及事件的性质，为以独立于模型的方式搜索这些新的物理场景铺平了道路。此外，由于该方法仅依赖于学习标准对象，因此可以从数据中轻松获得控制样本，因此可以以完全数据驱动的方式构建分析。

英文摘要：

Search for new physics events at the LHC mostly rely on the assumption that the events are characterized in terms of standard-reconstructed objects such as isolated photons, leptons, and jets initiated by QCD-partons. While such strategy works for a vast majority of physics beyond the standard model scenarios, there are examples aplenty where new physics give rise to anomalous objects (such as collimated and equally energetic particles, decays due to long lived particles etc.) in the detectors, which can not be classified as any of the standard-objects. Varied methods and search strategies have been proposed, each of which is trained and optimized for specific models, topologies, and model parameters. Further, as LHC keeps excluding all expected candidates for new physics, the need for a generic method/tool that is capable of finding the unexpected can not be understated. In this paper, we propose one such method that relies on the philosophy that all anomalous objects are $\it{not}$ standard-objects. The anomaly finder, we suggest, simply is a collection of vetoes that eliminate all standard-objects up to a pre-determined acceptance rate. Any event containing at least one anomalous object (that passes all these vetoes), can be identified as a candidate for new physics. Subsequent offline analyses can determine the nature of the anomalous object as well as of the event, paving a robust way to search for these new physics scenarios in a model-independent fashion. Further, since the method relies on learning only the standard-objects, for which control samples are readily available from data, one can build the analysis in an entirely data-driven way.

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。

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