Skip to content
Descubridor Institucional UMAR
Inicio
Búsqueda avanzada
Explorar
Inicio
Búsqueda avanzada
Explorar
Login
Language
English
Deutsch
Español
Français
Italiano
All Fields
Title
Author
Subject
Call Number
ISBN/ISSN
Tag
Find
Advanced
Multivariate Deep Learning Techniques for Optimizing Four-Top-Quark Signal Classification at CMS
Multivariate Deep Learning Techniques for Optimizing Four-Top-Quark Signal Classification at CMS
Fuente:
Zenodo
Saved in:
Bibliographic Details
Main Author:
Ali, Syed Haider
Format:
Recurso digital
Language:
English
Published:
Zenodo
2025
Online Access:
Acceder al recurso
Tags:
Add Tag
No Tags, Be the first to tag this record!
Cite this
Text this
Email this
Print
Export Record
Export to RefWorks
Export to EndNoteWeb
Export to EndNote
Save to List
Permanent link
Holdings
Description
Comments
Similar Items
Staff View
Description
Description not available.
Similar Items
Multivariate Deep Learning Techniques for Optimizing Four-Top-Quark Signal Classification at CMS
by: Ali, Syed Haider
Published: (2025)
Multivariate Deep Learning Techniques for Optimizing Four-Top-Quark Signal Classification at CMS
by: Ali, Syed Haider
Published: (2025)
Multivariate Deep Learning Techniques for Optimizing Four-Top-Quark Signal Classification at CMS (WIP)
by: Ali, Syed Haider
Published: (2025)
Machine Learning Approaches to Top Quark Flavor-Changing Four-Fermion Interactions in Trilepton Signals at the LHC
by: Bostanabad, Meisam Ghasemi, et al.
Published: (2025)
Four-Charm-Quark Matter from the CMS Collaboration as a Witness of the Development of High-Precision Hadron Spectroscopy
by: Liu, Xiang
Published: (2024)