# Multifractal detrended moving average analysis of Kepler stars with surface differential rotation traces

## TL;DR

This study applies multifractal analysis to Kepler star data to distinguish the dynamics of starspots and magnetic activity, revealing different complexity levels in stars with and without surface differential rotation.

## Contribution

It introduces a novel multifractal detrended moving average method to analyze stellar time series, uncovering distinct magnetic activity mechanisms linked to differential rotation.

## Key findings

- Starspots exhibit distinct dynamics in stars with and without differential rotation.
- Magnetic fields are governed by two mechanisms with different complexity levels.
- Differential rotation correlates with the degree of asymmetry in multifractal properties.

## Abstract

A multifractal formalism is employed to analyse high-precision time-series data of \textit{Kepler} stars with surface differential rotation traces. The multifractal detrended moving average analysis (MFDMA) algorithm has been explored to characterize the multi-scale behaviour of the observed time series from a sample of 662 stars selected with parameters close to those of the Sun, e.g., effective temperature, mass, effective gravity and rotation period. Among these stars, 141 have surface differential rotation traces, whereas 521 have no detected differential rotation signatures. In our sample, we also include the Sun in its active phase. Our results can be summarized in two points: first, our work suggests that starspots for time series with and without differential rotation have distinct dynamics, and second, the magnetic fields of active stars are apparently governed by two mechanisms with different levels of complexity for fluctuations. Throughout the course of the study, we identified an overall trend whereby the differential rotation is distributed in two $H$ regimes segregated by the degree of asymmetry $A$, where $H$-index denotes the global Hurst exponent which is used as a measure of long-term memory of time series. As a result, we show that the degree of asymmetry can be considered a segregation factor that distinguishes the differential rotation behaviour when related to the effect of the rotational modulation on the time series. In summary, the multifractality signals in our sample are the result of magnetic activity control mechanisms leading to activity-related long-term persistent signatures.