Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/87729
Título: | AtlFast3: The Next Generation of Fast Simulation in ATLAS |
Autor(es): | Castro, Nuno Filipe Onofre, A. ATLAS Collaboration |
Data: | 2022 |
Editora: | Springer |
Revista: | Computing and Software for Big Science |
Citação: | Aad, G., Abbott, B., Abbott, D. C., Abud, A. A., Abeling, K., Abhayasinghe, D. K., . . . Zwalinski, L. (2022). AtlFast3: The Next Generation of Fast Simulation in ATLAS.. Computing and Software for Big Science, 6(1). doi: 10.1007/s41781-021-00079-7 |
Resumo(s): | The ATLAS experiment at the Large Hadron Collider has a broad physics programme ranging from precision measurements to direct searches for new particles and new interactions, requiring ever larger and ever more accurate datasets of simulated Monte Carlo events. Detector simulation with Geant4 is accurate but requires significant CPU resources. Over the past decade, ATLAS has developed and utilized tools that replace the most CPU-intensive component of the simulation—the calorimeter shower simulation—with faster simulation methods. Here, AtlFast3, the next generation of high-accuracy fast simulation in ATLAS, is introduced. AtlFast3 combines parameterized approaches with machine-learning techniques and is deployed to meet current and future computing challenges, and simulation needs of the ATLAS experiment. With highly accurate performance and significantly improved modelling of substructure within jets, AtlFast3 can simulate large numbers of events for a wide range of physics processes. |
Tipo: | Artigo |
Descrição: | This manuscript has associated data in a data repository. [Author’s comment: “All ATLAS scientific output is published in journals, and preliminary results are made available in Conference Notes. All are openly available, without restriction on use by external parties beyond copyright law and the standard conditions agreed by CERN. Data associated with journal publications are also made available: tables and data from plots (e.g. cross section values, likelihood profiles, selection efficiencies, cross section limits, ...) are stored in appropriate repositories such as HEPDATA (http://hepdata.cedar.ac.uk/). ATLAS also strives to make additional material related to the paper available that allows a reinterpretation of the data in the context of new theoretical models. For example, an extended encapsulation of the analysis is often provided for measurements in the framework of RIVET (http://rivet.hepforge.org/).” This information is taken from the ATLAS Data Access Policy, which is a public document that can be downloaded from http://opendata.cern.ch/record/413[opendata.cern.ch].] |
URI: | https://hdl.handle.net/1822/87729 |
DOI: | 10.1007/s41781-021-00079-7 |
ISSN: | 2510-2036 |
e-ISSN: | 2510-2044 |
Versão da editora: | https://link.springer.com/article/10.1007/s41781-021-00079-7 |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
Aparece nas coleções: | LIP - Artigos/papers |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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s41781-021-00079-7.pdf | 9,5 MB | Adobe PDF | Ver/Abrir |
Este trabalho está licenciado sob uma Licença Creative Commons