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- W2012206988 abstract "We present a fast algorithm to identify both regular and irregular orbits that map out a sustained shape in configuration space. The method, which we dub ‘pattern autocorrelation’ (PACO), detects a repeating pattern in time-series constructed from binary sign changes in phase-space coordinates reduced to two dimensions. This is achieved by computing the autocorrelation function of the time-series, and by retrieving a pattern and a pattern-to-signal ratio. We apply the method to two-dimensional orbits in the logarithmic potential in an application to spiral galaxies with an asymptotically flat rotation curve; the general case of three-dimensional orbits is sketched. We find that irregular orbits can yet sustain the smooth morphological features of a galaxy for a substantial fraction of a Hubble time: this fraction is quantified through the pattern-to-signal ratio. In the case where a central supermassive black hole is added to the potential, we find that up to ≈16% of initial conditions space yields irregular motion that may sustain long-lived regular features. The method further detects and distinguishes orbits that are not based on Lissajous theory of resonant motion." @default.
- W2012206988 created "2016-06-24" @default.
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- W2012206988 date "2013-12-01" @default.
- W2012206988 modified "2023-10-18" @default.
- W2012206988 title "Classifying two-dimensional orbits using pattern recognition" @default.
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- W2012206988 doi "https://doi.org/10.1016/j.newast.2013.04.002" @default.
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