Markov properties of the magnetic field in the quiet solar photosphere

TL;DR

This study demonstrates that the magnetic field in the quiet solar photosphere behaves as a Markov process, indicating it is a memoryless stochastic variable, based on analysis of high-resolution observational data.

Contribution

It introduces a method to analyze solar magnetic field stochasticity without relying on feature-tracking or subjective assumptions, establishing its Markovian nature from observational data.

Findings

Magnetic field components satisfy Markov chain conditions.

Magnetic field can be modeled as a memoryless stochastic process.

Analysis applied to high-resolution IMaX data with 33 sec cadence.

Abstract

The observed magnetic field on the solar surface is characterized by a very complex spatial and temporal behaviour. Although feature-tracking algorithms have allowed us to deepen our understanding of this behaviour, subjectivity plays an important role in the identification, tracking of such features. In this paper we study the temporal stochasticity of the magnetic field on the solar surface \textit{without} relying neither on the concept of magnetic feature nor on subjective assumptions about their identification and interaction. The analysis is applied to observations of the magnetic field of the quiet solar photosphere carried out with the IMaX instrument on-board the stratospheric balloon {\sc Sunrise}. We show that the joint probability distribution functions of the longitudinal ($B_\parallel$) and transverse ($B_\perp$) components of the magnetic field, as well as of the magnetic pressure ($B^2=B^2_\perp+B^2_\parallel$), verify the necessary and sufficient condition for the Markov chains. Therefore we establish that the magnetic field, as seen by IMaX with a resolution of 0.15\arcsec-0.18\arcsec and $33$~sec cadence, can be considered as a memoryless temporal fluctuating quantity.