Abstract
Basins of attraction take its name from hydrology, and in dynamical systems they refer to the set of initial conditions that lead to a particular final state. When different final states are possible, the predictability of the system depends on the structure of these basins. We introduce the concept of basin entropy, that aims to quantify the final state unpredictability associated to the basins. Using several paradigmatic examples from nonlinear dynamics, we dissect the meaning of this new quantity and suggest some useful applications such as the basin entropy parameter set. Then, we explain how it is possible to apply this concept to experiments with cold atoms. Previous works pointed out that chaotic dynamics could be at the heart of some interesting regimes found in the scattering of cold atoms. Here, we detail how one of the hallmarks of chaos, the appearance of fractal structures in phase space, can be detected directly from experimental measurements thanks to the basin entropy.
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Notes
- 1.
In this work we have normalized the region of the phase space, so that the values of the scaling box size \(\varepsilon \) in the plots are the inverse of the number of pixels used as a grid.
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Acknowledgements
This work was supported by the Spanish Ministry of Economy and Competitiveness under Project No. FIS2013-40653-P and by the Spanish State Research Agency (AEI) and the European Regional Development Fund (FEDER) under Project No. FIS2016-76883-P. MAFS acknowledges the jointly sponsored financial support by the Fulbright Program and the Spanish Ministry of Education (Program No. FMECD-ST-2016). Financial support from the Programme Investissements d’Avenir under the program ANR-11-IDEX-0002-02, reference ANR-10-LABX-0037-NEXT is also acknowledged.
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Daza, A., Wagemakers, A., Georgeot, B., Guéry-Odelin, D., Sanjuán, M.A.F. (2018). Basin Entropy, a Measure of Final State Unpredictability and Its Application to the Chaotic Scattering of Cold Atoms. In: Edelman, M., Macau, E., Sanjuan, M. (eds) Chaotic, Fractional, and Complex Dynamics: New Insights and Perspectives. Understanding Complex Systems. Springer, Cham. https://doi.org/10.1007/978-3-319-68109-2_2
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