SCYNet: testing supersymmetric models at the LHC with neural networks

Location
Deutsche Nationalbibliothek Frankfurt am Main
ISSN
1434-6052
Extent
Online-Ressource
Language
Englisch
Notes
online resource.

Bibliographic citation
SCYNet: testing supersymmetric models at the LHC with neural networks ; volume:77 ; number:10 ; day:25 ; month:10 ; year:2017 ; pages:1-20 ; date:10.2017
The European physical journal / C. C, Particles and fields ; 77, Heft 10 (25.10.2017), 1-20, 10.2017

Classification
Physik

Creator
Bechtle, Philip
Contributor
Belkner, Sebastian
Dercks, Daniel
Hamer, Matthias
Keller, Tim
Krämer, Michael
Sarrazin, Björn
Schütte-Engel, Jan
Tattersall, Jamie
SpringerLink (Online service)

DOI
10.1140/epjc/s10052-017-5224-8
URN
urn:nbn:de:1111-201802062578
Rights
Der Zugriff auf das Objekt ist unbeschränkt möglich.
Last update
14.08.2025, 10:55 AM CEST

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Associated

  • Bechtle, Philip
  • Belkner, Sebastian
  • Dercks, Daniel
  • Hamer, Matthias
  • Keller, Tim
  • Krämer, Michael
  • Sarrazin, Björn
  • Schütte-Engel, Jan
  • Tattersall, Jamie
  • SpringerLink (Online service)

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