A field theory approach to the statistical kinematic dynamo

TL;DR

This paper develops a stochastic field theory framework for understanding the long-term evolution of the geomagnetic field, linking statistical properties of magnetic field generation to paleomagnetic observations.

Contribution

It introduces a novel stochastic dynamo model using MSRJD formalism to analyze magnetic field statistics and turbulent diffusivity, bridging theory and paleomagnetic data.

Findings

Derived the average magnetic response function using MSRJD formalism

Estimated turbulent magnetic diffusivity consistent with mean-field theory

Provided a new stochastic approach for long-term geomagnetic field analysis

Abstract

Variations in the geomagnetic field occur on a vast range of time scales, from milliseconds to millions of years. The advent of satellite measurements has allowed for detailed studies of the short timescale geomagnetic field behaviour, but understanding the long timescale evolution remains challenging due to the sparsity of the paleomagnetic record. This paper introduces a field theory framework for studying magnetic field generation as a result of stochastic fluid motions. By constructing a stochastic kinematic dynamo model, we derive statistical properties of the magnetic field that may be compared to observations from the paleomagnetic record. The fluid velocity is taken to act as a random forcing obeying Gaussian statistics. Using the Martin-Siggia-Rose-Janssen-de Dominicis (MSRJD) formalism, we compute the average magnetic field response function. From this we obtain an estimate for the turbulent contribution to the magnetic diffusivity, and find that it is consistent with results from mean-field dynamo theory. This framework presents much promise for studying the geomagnetic field in a stochastic context.