# Physical insights on fractal analysis applied to light curves $\delta$ Scuti stars

## TL;DR

This paper demonstrates that fractal analysis of light curves from { extdelta} Scuti stars reveals a significant fractal background influencing frequency detection, and introduces CGSA as a tool for improved mode extraction and analysis.

## Contribution

It introduces the use of CGSA to distinguish fractal noise from harmonic signals in stellar light curves, enhancing pulsation frequency detection methods.

## Key findings

- Fractal background significantly affects frequency analysis in { extdelta} Scuti stars.
- CGSA effectively separates stochastic fractal noise from harmonic oscillations.
- A new criterion for pre-whitening termination based on fractal component percentage.

## Abstract

Fractal fingerprints have been found recently in the light curves of several {\delta} Scuti stars observed by CoRoT satellite. This sole fact might pose a problem for the detection of pulsation frequencies using classical pre-whitening techniques, but it is also a potentially rich source for information about physical mechanisms associated to stellar variability. Assuming that a light curve is composed by a superposition of oscillation modes with a fractal background noise, in this work we applied the Coarse Graining Spectral Analysis (CGSA), an FFT based algorithm, which can discriminate in a time series the stochastic fractal power spectra from the harmonic one. We have found that the fractal background component is determining the frequency content extracted using classical pre-whitening techniques in the light curves of {\delta} Scuti stars. This might be crucial to understand the amount of frequencies excited in these kind of pulsating stars. Additionally, CGSA resulted to be relevant in order to extract the oscillation modes, this points to a new criterion to stop the pre-whitening cascade based on the percentage of fractal component in the residuals.