论文标题
短暂学习 - 通过在线训练的正常流量增强触发器
Ephemeral Learning -- Augmenting Triggers with Online-Trained Normalizing Flows
论文作者
论文摘要
LHC的大数据速率需要在线触发系统来选择相关碰撞。我们建议不要一次压缩各个事件，而是建议一次压缩整个数据集。我们使用标准化流程作为深层生成模型，以在线了解数据的概率密度。然后，事件由生成神经网络表示，可以离线检查异常或用于其他分析目的。我们为玩具模型和相关增强的凸起狩猎展示了我们的新方法。
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.
