Extending the search for new resonances with machine learning
Extending the search for new resonances with machine learning
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DOI：
10.1103/physrevd.99.014038
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发表时间：
2019-01-28
影响因子：
5
通讯作者：
Nachman, Benjamin
中科院分区：
文献类型：
--
作者：
Collins, Jack H.;Howe, Kiel;Nachman, Benjamin
寻找新粒子最古老、最可靠的技术是在平稳下降的背景上寻找不变质谱中的“凸起”。我们提出了凹凸搜索的新扩展，它自然受益于现代机器学习算法，同时保持模型不可知性。该方法基于无标签分类 (CWoLa) 方法，其中使用不变质量来创建两个可能混合的样本，一个具有很少或没有信号，另一个具有潜在共振。与不变质量不相关的附加特征可用于训练分类器。鉴于大型强子对撞机 (LHC) 缺乏新的物理信号，这种与模型无关的方法对于确保完全覆盖以充分利用 LHC 实验的丰富数据集至关重要。除了说明新方法如何在简单的测试用例中工作之外，我们还展示了扩展凹凸搜索在现有技术无法覆盖的通道中进行真实全强子共振搜索的强大功能。
The oldest and most robust technique to search for new particles is to look for " bumps" in invariant mass spectra over smoothly falling backgrounds. We present a new extension of the bump hunt that naturally benefits from modern machine learning algorithms while remaining model agnostic. This approach is based on the classification without labels (CWoLa) method where the invariant mass is used to create two potentially mixed samples, one with little or no signal and one with a potential resonance. Additional features that are uncorrelated with the invariant mass can be used for training the classifier. Given the lack of new physics signals at the Large Hadron Collider (LHC), such model-agnostic approaches are critical for ensuring full coverage to fully exploit the rich datasets from the LHC experiments. In addition to illustrating how the new method works in simple test cases, we demonstrate the power of the extended bump hunt on a realistic all-hadronic resonance search in a channel that would not be covered with existing techniques.