PECCARY: A novel approach for characterizing orbital complexity, stochasticity, and regularity

TL;DR

PECCARY is a computationally efficient statistical method for characterizing the complexity, regularity, and stochasticity of time-series data, with applications demonstrated in astrophysics and other fields.

Contribution

This paper introduces PECCARY, a novel method for analyzing orbital systems, optimized for astrophysical data, and provides a Python implementation for broad application.

Findings

PECCARY effectively detects chaos signatures in short and noisy time-series.

The method successfully characterizes various physical and mathematical systems.

An optimal sampling scheme for analysis is proposed.

Abstract

Permutation Entropy and statistiCal Complexity Analysis for astRophYsics (PECCARY) is a computationally inexpensive, statistical method by which any time-series can be characterized as predominantly regular, complex, or stochastic. Elements of the PECCARY method have been used in a variety of physical, biological, economic, and mathematical scenarios, but have not yet gained traction in the astrophysical community. This study introduces the PECCARY technique with the specific aims to motivate its use in and optimize it for the analysis of astrophysical orbital systems. PECCARY works by decomposing a time-dependent measure, such as the x-coordinate or orbital angular momentum time-series, into ordinal patterns. Due to its unique approach and statistical nature, PECCARY is well-suited for detecting preferred and forbidden patterns (a signature of chaos), even when the chaotic behavior is short-lived or when working with a relatively short duration time-series or small sets of time-series data. A variety of examples are used to demonstrate the capabilities of PECCARY. These include mathematical examples (sine waves, varieties of noise, sums of sine waves, well-known chaotic functions), a double pendulum system, and astrophysical tracer particle simulations with potentials of varying intricacies. Since the adopted timescale used to diagnose a given time-series can affect the outcome, a method is presented to identify an ideal sampling scheme, constrained by the overall duration and the natural timescale of the system. The accompanying PECCARY Python package and its usage are discussed.