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A method for inferring signal strength modifiers by conditional invertible neural networks

; ; ; ;

In
26th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2023) : Norfolk, VA, USA, May 8-12, 2023 / R. De Vita, X. Espinal, P. Laycock and O. Shadura (eds.), Seiten/Artikel-Nr: 09001, [1]-7

Konferenz/Event:26. International Conference on Computing in High Energy and Nuclear Physics , Norfolk, VA , USA , CHEP 2023 , 2023-05-08 - 2023-05-12

ImpressumLes Ulis : EDP Sciences

Umfang09001, [1]-7

ReiheThe European physical journal. Web of Conferences ; 295

Online
DOI: 10.1051/epjconf/202429509001

DOI: 10.18154/RWTH-2024-10548
URL: https://publications.rwth-aachen.de/record/996357/files/996357.pdf

Einrichtungen

  1. Lehr- und Forschungsgebiet Experimentalphysik (Hochenergiephysik) (133320)
  2. Fachgruppe Physik (130000)
  3. III. Physikalisches Institut A (133005)
  4. Lehr- und Forschungsgebiet Theoretische Teilchenphysik (136520)


Thematische Einordnung (Klassifikation)
DDC: 530

OpenAccess:
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Dokumenttyp
Contribution to a book/Contribution to a conference proceedings

Format
online

Sprache
English

Anmerkung
Peer reviewed article

Externe Identnummern
WOS Core Collection: WOS:001244151902052
SCOPUS: SCOPUS:2-s2.0-85212174191

Interne Identnummern
RWTH-2024-10548
Datensatz-ID: 996357

Beteiligte Länder
Germany

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Document types > Books > Contributions to a book
Faculty of Mathematics, Computer Science and Natural Sciences (Fac.1) > Department of Physics
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130000
136520
133320
 Record created 2024-11-11, last modified 2025-07-28
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