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: | , , , , , , |
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| 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 |
| Summary: | 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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| Item Description: | Version 1 vom 28 Junuar 2022, Version 2 vom 18 Februar 2022 Gesehen am 15.09.2022 |
| Physical Description: | Online Resource |
| DOI: | 10.48550/arXiv.2202.09375 |