Diagnosing Turbulence in the Neutral and Molecular Interstellar Medium of Galaxies

TL;DR

This paper reviews statistical and machine learning methods for diagnosing magnetohydrodynamic turbulence in the interstellar medium of galaxies, emphasizing their application to observational data and simulations.

Contribution

It introduces and discusses advanced statistical diagnostics and machine learning algorithms tailored for analyzing turbulence in the interstellar medium, integrating observations and simulations.

Findings

Effective techniques for estimating turbulence parameters from observational data

Validation of methods using numerical simulations of MHD turbulence

Provision of open-source tools for the astrophysical community

Abstract

Magnetohydrodynamic (MHD) turbulence is a crucial component of the current paradigms of star formation, dynamo theory, particle transport, magnetic reconnection and evolution of structure in the interstellar medium (ISM) of galaxies. Despite the importance of turbulence to astrophysical fluids, a full theoretical framework based on solutions to the Navier-Stokes equations remains intractable. Observations provide only limited line-of-sight information on densities, temperatures, velocities and magnetic field strengths and therefore directly measuring turbulence in the ISM is challenging. A statistical approach has been of great utility in allowing comparisons of observations, simulations and analytic predictions. In this review article we address the growing importance of MHD turbulence in many fields of astrophysics and review statistical diagnostics for studying interstellar and interplanetary turbulence. In particular, we will review statistical diagnostics and machine learning algorithms that have been developed for observational data sets in order to obtain information about the turbulence cascade, fluid compressibility (sonic Mach number), and magnetization of fluid (Alfv\'enic Mach number). These techniques have often been tested on numerical simulations of MHD turbulence, which may include the creation of synthetic observations, and are often formulated on theoretical expectations for compressible magnetized turbulence. We stress the use of multiple techniques, as this can provide a more accurate indication of the turbulence parameters of interest. We conclude by describing several open-source tools for the astrophysical community to use when dealing with turbulence.