  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.
