Speaker
Sascha Daniel Diefenbacher
(Hamburg University (DE))
Description
The large data rates at the LHC make it impossible to store every single observed interaction. Therefore we require an online trigger system to select relevant collisions. We propose an additional approach, where rather than compressing individual events, we compress the 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.
Primary authors
Anja Butter
Sascha Daniel Diefenbacher
(Hamburg University (DE))
Ben Nachman
(Lawrence Berkeley National Lab. (US))
David Shih
Gregor Kasieczka
(Hamburg University (DE))
Ramon Winterhalder
(UC Louvain)
Tilman Plehn
