短暂学习--用在线训练的规范化流增强触发器
Ephemeral Learning -- Augmenting Triggers with Online-Trained Normalizing Flows
摘要：
大型强子对撞机的大数据速率需要一个在线触发系统来选择相关的碰撞。我们建议一次压缩整个数据集，而不是压缩单个事件。我们使用归一化流作为深度生成模型来在线学习数据的概率密度。然后，事件由生成神经网络表示，并且可以离线检查异常或用于其他分析目的。我们展示了我们的新方法的玩具模型和相关性增强凹凸狩猎。
英文摘要：
The large data rates at the LHC require an online trigger system to select relevant collisions. Rather than compressing individual events, we propose to compress an entire data set at once. We use a normalizing flow as a deep generative model to learn the probability density of the data online. The events are then represented by the generative neural network and can be inspected offline for anomalies or used for other analysis purposes. We demonstrate our new approach for a toy model and a correlation-enhanced bump hunt.
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。
