Ephemeral learning: augmenting triggers with online-trained normalizing flows
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: | , , , , , , |
|---|---|
| Format: | Article (Journal) Chapter/Article |
| Language: | English |
| Published: |
28 Jun 2022
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| Edition: | Version v2 |
| In: |
Arxiv
Year: 2022, Pages: 1-17 |
| DOI: | 10.48550/arXiv.2202.09375 |
| Online Access: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.48550/arXiv.2202.09375 Verlag, lizenzpflichtig, Volltext: http://arxiv.org/abs/2202.09375 
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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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