论文标题
结合离群分析算法以识别LHC的新物理
Combining outlier analysis algorithms to identify new physics at the LHC
论文作者
论文摘要
到目前为止，在大型强子对撞机上缺乏新物理学的证据促使了与模型无关的搜索技术的发展。在这项研究中，我们比较了各种异常检测技术的异常评分：隔离森林，高斯混合模型，静态自动编码器和$β$ - 变量自动编码器（VAE），我们在其中定义了后者的重建损失，将其定义为回归和分类术语的重量组合。我们将这些算法应用于模拟LHC数据的4个向量，但还研究了当将非VAE算法应用于VAE产生的潜在空间变量时的性能。此外，我们评估了这些算法的异常得分以各种方式组合的性能。使用超对称基准点，我们发现在VAE的潜在空间中训练的算法产生的异常得分的逻辑和组合是所有测试方法中最有效的歧视者。
The lack of evidence for new physics at the Large Hadron Collider so far has prompted the development of model-independent search techniques. In this study, we compare the anomaly scores of a variety of anomaly detection techniques: an isolation forest, a Gaussian mixture model, a static autoencoder, and a $β$-variational autoencoder (VAE), where we define the reconstruction loss of the latter as a weighted combination of regression and classification terms. We apply these algorithms to the 4-vectors of simulated LHC data, but also investigate the performance when the non-VAE algorithms are applied to the latent space variables created by the VAE. In addition, we assess the performance when the anomaly scores of these algorithms are combined in various ways. Using supersymmetric benchmark points, we find that the logical AND combination of the anomaly scores yielded from algorithms trained in the latent space of the VAE is the most effective discriminator of all methods tested.
