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arXiv · 2202.09375

Ephemeral Learning -- Augmenting Triggers with Online-Trained Normalizing Flows

Anja Butter·Sascha Diefenbacher·Gregor Kasieczka·Benjamin Nachman·Tilman Plehn·David Shih·Ramon Winterhalder
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Abstract

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.

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Anja Butter, Sascha Diefenbacher, Gregor Kasieczka, Benjamin Nachman, Tilman Plehn, David Shih, Ramon Winterhalder. 2022-02-18. Ephemeral Learning -- Augmenting Triggers with Online-Trained Normalizing Flows. https://doi.org/10.21468/scipostphys.13.4.087

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