Bayesian approach to equipartition estimation of magnetic field strength

TL;DR

This paper introduces a Bayesian method for estimating magnetic field strength in galaxies from radio observations, accounting for uncertainties and complex spectra, improving upon traditional equipartition techniques.

Contribution

It presents a novel Bayesian framework for calculating equipartition magnetic fields that incorporates uncertainties and complex CR spectra, extending previous simplified methods.

Findings

Bayesian approach provides uncertainty estimates for magnetic field strength.

Method accommodates complex CR spectra and mixed magnetic field components.

Web application BMAG implements the new equipartition estimation method.

Abstract

Magnetic fields, together with cosmic rays (CRs), play an important role in the dynamics and evolution of galaxies, but are difficult to estimate. Energy equipartition between magnetic fields and CRs provides a convenient way to approximate magnetic field strength from radio observations. We present a new approach for calculating the equipartition magnetic field strength based on Bayesian methods. In this approach, the magnetic field is a random variable that is distributed according to a posterior distribution conditional on synchrotron emission and the size of the emitting region. It allows the direct application of the general formulas for total and polarized synchrotron radiation without the need to invert these formulas, which has limited the equipartition method to highly simplified cases. We have derived the equipartition condition for the case of different low-energy breaks, slopes, and high-energy cutoffs of power law spectra of the CR proton and electron distributions. The derived formalism was applied in the general case of a magnetic field consisting of both uniform and randomly oriented field components. The applied Bayesian approach naturally provides the uncertainties in the estimated magnetic field strengths resulting from the uncertainties in the observables and the assumed values of the unknown physical parameters. In the examples presented, we used two different Markov Chain Monte Carlo methods to generate the posterior distribution of the magnetic field. We have also developed a web application called BMAG that implements the described approach for different models and observational parameters of real sources.