潜空间中的碰撞搜索
Bump Hunting in Latent Space
摘要：
无监督的异常检测在未来的分析中可能是至关重要的，在大型数据集中搜索罕见的现象，例如在LHC收集。为此，我们引入了一个物理启发的变分自动编码器（VAE）架构，该架构在LHC奥运会机器学习挑战数据集上具有竞争力和鲁棒性。我们演示了如何将一些物理观测值直接嵌入到VAE潜在空间中，同时保持分类器对它们明显不可知，可以帮助识别和识别由数据集中存在异常引起的测量光谱中的特征。
英文摘要：
Unsupervised anomaly detection could be crucial in future analyses searching for rare phenomena in large datasets, as for example collected at the LHC. To this end, we introduce a physics inspired variational autoencoder (VAE) architecture which performs competitively and robustly on the LHC Olympics Machine Learning Challenge datasets. We demonstrate how embedding some physical observables directly into the VAE latent space, while at the same time keeping the classifier manifestly agnostic to them, can help to identify and characterise features in measured spectra as caused by the presence of anomalies in a dataset.
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。
