Understanding event-generation networks via uncertainties
Following the growing success of generative neural networks in LHC simulations, the crucial question is how to control the networks and assign uncertainties to their event output. We show how Bayesian normalizing flow or invertible networks capture uncertainties from the training and turn them into...
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| Main Authors: | , , , |
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| Format: | Article (Journal) Chapter/Article |
| Language: | English |
| Published: |
October 4, 2021
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| In: |
Arxiv
Year: 2021, Pages: 1-26 |
| DOI: | 10.48550/arXiv.2104.04543 |
| Online Access: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.48550/arXiv.2104.04543 Verlag, lizenzpflichtig, Volltext: http://arxiv.org/abs/2104.04543 
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| Author Notes: | Marco Bellagente, Manuel Haußmann, Michel Luchmann, and Tilman Plehn |
| Summary: | Following the growing success of generative neural networks in LHC simulations, the crucial question is how to control the networks and assign uncertainties to their event output. We show how Bayesian normalizing flow or invertible networks capture uncertainties from the training and turn them into an uncertainty on the event weight. Fundamentally, the interplay between density and uncertainty estimates indicates that these networks learn functions in analogy to parameter fits rather than binned event counts. |
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| Item Description: | Gesehen am 13.07.2022 |
| Physical Description: | Online Resource |
| DOI: | 10.48550/arXiv.2104.04543 |