Recently, the concept of weakly supervised learning has gained popularity in the high-energy physics community due to its ability to learn even with a noisy and impure dataset. This method is valuable in the quest to discover the elusive beyond Standard Model (BSM) particle. Nevertheless, the weakly supervised learning method still requires a learning sample that describes the features of the BSM particle truthfully to the classification model. Even with the various theoretical framework such as supersymmetry and the quantum black hole, creating a BSM sample is not a trivial task since the exact feature of the particle is unknown. Due to these difficulties, we propose an alternative classifier type called the one-class classification (OCC). OCC algorithms require only background or noise samples in its training dataset, which is already abundant in the high-energy physics community. The algorithm will flag any sample that does not fit the background feature as an abnormality. In this paper, we introduce two new algorithms called EHRA and C-EHRA, which use machine learning regression and clustering to detect anomalies in samples. We tested the algorithms’ capability to create distinct anomalous patterns in the presence of BSM samples and also compare their classification output metrics to the Isolation Forest (ISF), a well-known anomaly detection algorithm. Five Monte Carlo supersymmetry datasets with the signal to noise ratio equal to 1, 0.1, 0.01, 0.001, and 0.0001 were used to test EHRA, C-EHRA and ISF algorithm. In our study, we found that the EHRA with an artificial neural network regression has the highest ROC-AUC score at 0.7882 for the balanced dataset, while the C-EHRA has the highest precision-sensitivity score for the majority of the imbalanced datasets. These findings highlight the potential use of the EHRA, C-EHRA, and other OCC algorithms in the quest to discover BSM particles.

中文翻译：

---

通过半监督学习在高能物理中进行异常检测的替代方法

最近，弱监督学习的概念在高能物理社区中得到了普及，因为它即使在嘈杂和不纯的数据集下也能学习。这种方法对于发现难以捉摸的超标准模型 (BSM) 粒子非常有价值。尽管如此，弱监督学习方法仍然需要一个将 BSM 粒子的特征真实描述给分类模型的学习样本。即使有各种理论框架，如超对称和量子黑洞，创建 BSM 样本也不是一件容易的事，因为粒子的确切特征是未知的。由于这些困难，我们提出了一种替代分类器类型，称为一类分类（OCC）。OCC 算法只需要其训练数据集中的背景或噪声样本，这在高能物理界已经很丰富了。该算法会将任何不符合背景特征的样本标记为异常。在本文中，我们介绍了两种称为 EHRA 和 C-EHRA 的新算法，它们使用机器学习回归和聚类来检测样本中的异常。我们测试了算法在存在 BSM 样本的情况下创建不同异常模式的能力，并将其分类输出指标与著名的异常检测算法 Isolation Forest (ISF) 进行比较。使用信噪比分别为 1、0.1、0.01、0.001 和 0.0001 的五个 Monte Carlo 超对称数据集来测试 EHRA、C-EHRA 和 ISF 算法。在我们的研究中，我们发现具有人工神经网络回归的 EHRA 在平衡数据集的 ROC-AUC 得分最高，为 0.7882，而对于大多数不平衡数据集，C-EHRA 的精度敏感度得分最高。这些发现突出了 EHRA、C-EHRA 和其他 OCC 算法在发现 BSM 粒子中的潜在用途。