A Data-Directed Paradigm for BSM searches
A Data-Directed Paradigm for BSM searches
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BSM 搜索的数据导向范式
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
S. Bressler
中科院分区：
文献类型：
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作者：
S. Volkovich;Federico De Vito Halevy;S. Bressler
我们提出了一种新的数据导向范式（DDP）来寻找新的物理。专注于数据，排他性的选择，表现出显着的偏离标准模型的已知属性，可以有效地识别和标记为进一步的研究。DDP可以利用不同的属性。在这里，通过将神经网络（NN）的潜力与常见的颠簸狩猎方法相结合来证明该范例。使用神经网络，资源消耗的任务的背景和系统的不确定性估计被避免，允许快速测试的许多最终状态，只有轻微的退化，相对于标准分析方法的颠簸的灵敏度。
We propose a novel data-directed paradigm (DDP) to search for new physics. Focusing on the data, exclusive selections which exhibit significant deviations from known properties of the standard model can be identified efficiently and marked for further study. Different properties can be exploited with the DDP. Here, the paradigm is demonstrated by combining the promising potential of neural networks (NN) with the common bump-hunting approach. Using the NN, the resource-consuming tasks of background and systematic uncertainty estimation are avoided, allowing rapid testing of many final states with only a minor degradation in the sensitivity to bumps relative to standard analysis methods.