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Ephemeral learning: augmenting triggers with online-trained normalizing flows

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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...

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Main Authors: Butter, Anja (Author) , Diefenbacher, Sascha (Author) , Kasieczka, Gregor (Author) , Nachman, Benjamin (Author) , Plehn, Tilman (Author) , Shih, David (Author) , Winterhalder, Ramon (Author)
Format: Article (Journal)
Language:English
Published: 07-10-2022
In: SciPost physics
Year: 2022, Volume: 13, Issue: 4, Pages: 1-17
ISSN:2542-4653
DOI:10.21468/SciPostPhys.13.4.087
Online Access:Verlag, lizenzpflichtig, Volltext: https://doi.org/10.21468/SciPostPhys.13.4.087
Verlag, lizenzpflichtig, Volltext: https://scipost.org/10.21468/SciPostPhys.13.4.087
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Author Notes:Anja Butter, Sascha Diefenbacher, Gregor Kasieczka, Benjamin Nachman, Tilman Plehn, David Shih and Ramon Winterhalder
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Ephemeral learning: augmenting triggers with online-trained normalizing flows by Butter, Anja (Author) , Diefenbacher, Sascha (Author) , Kasieczka, Gregor (Author) , Nachman, Benjamin (Author) , Plehn, Tilman (Author) , Shih, David (Author) , Winterhalder, Ramon (Author) ,


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