Improved extrapolation methods of data-driven background estimations in high energy physics
- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- ISSN
-
1434-6052
- Extent
-
Online-Ressource
- Language
-
Englisch
- Notes
-
online resource.
- Bibliographic citation
-
Improved extrapolation methods of data-driven background estimations in high energy physics ; volume:81 ; number:7 ; day:22 ; month:7 ; year:2021 ; pages:1-7 ; date:7.2021
The European physical journal / C. C, Particles and fields ; 81, Heft 7 (22.7.2021), 1-7, 7.2021
- Creator
-
Choi, Suyong
Oh, Hayoung
- Contributor
-
SpringerLink (Online service)
- DOI
-
10.1140/epjc/s10052-021-09404-1
- URN
-
urn:nbn:de:101:1-2021121722404336546502
- Rights
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
15.08.2025, 7:25 AM CEST
Data provider
This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Choi, Suyong
- Oh, Hayoung
- SpringerLink (Online service)
Other Objects (12)
Non-resonant anomaly detection with background extrapolation
Atmospheric physics : background - methods - trends
Data-driven solutions and parameter estimations of a family of higher-order KdV equations based on physics informed neural networks
Unjustified extrapolation
Non-parametric data-driven background modelling using conditional probabilities
Estimations approximatives
Data-driven scale extrapolation: estimating yearly discharge for a large region by small sub-basins
Asymptotics and extrapolation
Liste des Estimations
Proxying ability by family background in returns to schooling estimations is generally a bad idea
Physics-Informed and Data-Driven Probabilistic Modeling of Materials Systems
Extrapolation of cavitation and hydrodynamic pressure in lubricated contacts: a physics-informed neural network approach
Non-resonant anomaly detection with background extrapolation
Atmospheric physics : background - methods - trends
Data-driven solutions and parameter estimations of a family of higher-order KdV equations based on physics informed neural networks
Unjustified extrapolation
Non-parametric data-driven background modelling using conditional probabilities
Estimations approximatives
Data-driven scale extrapolation: estimating yearly discharge for a large region by small sub-basins
Asymptotics and extrapolation
Liste des Estimations
Proxying ability by family background in returns to schooling estimations is generally a bad idea
Physics-Informed and Data-Driven Probabilistic Modeling of Materials Systems
Extrapolation of cavitation and hydrodynamic pressure in lubricated contacts: a physics-informed neural network approach
Non-resonant anomaly detection with background extrapolation
Atmospheric physics : background - methods - trends
Data-driven solutions and parameter estimations of a family of higher-order KdV equations based on physics informed neural networks
Unjustified extrapolation
Non-parametric data-driven background modelling using conditional probabilities
Estimations approximatives
Data-driven scale extrapolation: estimating yearly discharge for a large region by small sub-basins
Asymptotics and extrapolation
Liste des Estimations
Proxying ability by family background in returns to schooling estimations is generally a bad idea
Physics-Informed and Data-Driven Probabilistic Modeling of Materials Systems