| Autor: |
D. van Dyk, F. Beaujean, T. Blake, C. Bobeth, M. Bordone, K. Dugic, E. Eberhard, N. Gubernari, E. Graverini, M. Jung, A. Kokulu, S. Kürten, D. Leljak, P. Lüghausen, S. Meiser, M. Rahimi, M. Reboud, R. Silva Coutinho, J. Virto, K. K. Vos, The EOS Authors |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
European Physical Journal C: Particles and Fields, Vol 82, Iss 6, Pp 1-30 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
1434-6052 |
| DOI: |
10.1140/epjc/s10052-022-10177-4 |
| Popis: |
Abstract EOS is an open-source software for a variety of computational tasks in flavor physics. Its use cases include theory predictions within and beyond the Standard Model of particle physics, Bayesian inference of theory parameters from experimental and theoretical likelihoods, and simulation of pseudo events for a number of signal processes. EOS ensures high-performance computations through a C++ back-end and ease of usability through a Python front-end. To achieve this flexibility, EOS enables the user to select from a variety of implementations of the relevant decay processes and hadronic matrix elements at run time. In this article, we describe the general structure of the software framework and provide basic examples. Further details and in-depth interactive examples are provided as part of the EOS online documentation. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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