物理分析中的异常检测与少监督学习
Anomaly Detection for Physics Analysis and Less than Supervised Learning
摘要：
现代机器学习工具提供了令人兴奋的可能性，可以定性地改变新粒子搜索的范式。特别是，新方法可以通过直接从数据中学习来获得对不可预见场景的敏感性，从而扩大搜索程序。新的想法有了显著的增长，它们刚刚开始应用于实验数据。本章介绍这些新的异常检测方法，从完全监督算法到无监督算法，包括弱监督方法。
英文摘要：
Modern machine learning tools offer exciting possibilities to qualitatively change the paradigm for new particle searches. In particular, new methods can broaden the search program by gaining sensitivity to unforeseen scenarios by learning directly from data. There has been a significant growth in new ideas and they are just starting to be applied to experimental data. This chapter introduces these new anomaly detection methods, which range from fully supervised algorithms to unsupervised, and include weakly supervised methods.
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。
