Deep-learning top taggers or the end of QCD?
Machine learning based on convolutional neural networks can be used to study jet images from the LHC. Top tagging in fat jets offers a well-defined framework to establish our DeepTop approach and compare its performance to QCD-based top taggers. We first optimize a network architecture to identify t...
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| Hauptverfasser: | , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
May 2, 2017
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| In: |
Journal of high energy physics
Year: 2017, Heft: 5 |
| ISSN: | 1029-8479 |
| DOI: | 10.1007/JHEP05(2017)006 |
| Online-Zugang: | Verlag, kostenfrei, Volltext: http://dx.doi.org/10.1007/JHEP05(2017)006 Verlag, kostenfrei, Volltext: https://link.springer.com/article/10.1007/JHEP05(2017)006 
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| Verfasserangaben: | Gregor Kasieczka, Tilman Plehn, Michael Russell and Torben Schell |
| Zusammenfassung: | Machine learning based on convolutional neural networks can be used to study jet images from the LHC. Top tagging in fat jets offers a well-defined framework to establish our DeepTop approach and compare its performance to QCD-based top taggers. We first optimize a network architecture to identify top quarks in Monte Carlo simulations of the Standard Model production channel. Using standard fat jets we then compare its performance to a multivariate QCD-based top tagger. We find that both approaches lead to comparable performance, establishing convolutional networks as a promising new approach for multivariate hypothesis-based top tagging. |
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| Beschreibung: | Gesehen am 26.09.2017 |
| Beschreibung: | Online Resource |
| ISSN: | 1029-8479 |
| DOI: | 10.1007/JHEP05(2017)006 |