Machine-Learning Estimation of Energy Fractions in MHD Turbulence Modes

TL;DR

This paper develops a machine learning method to accurately estimate the energy distribution among MHD turbulence modes in the interstellar medium from spectroscopic data, addressing observational limitations.

Contribution

It introduces a conditional Residual Neural Network trained on simulations to infer MHD mode energy fractions directly from spectroscopic maps, improving analysis of astrophysical turbulence.

Findings

Alfvén and slow modes dominate the energy budget in multiphase media.

Fast mode energy fraction increases in multiphase simulations compared to isothermal.

The model achieves high accuracy in recovering mode fractions from spectroscopic data.

Abstract

Magnetohydrodynamic (MHD) turbulence plays a central role in many astrophysical processes in the interstellar medium (ISM), including star formation and cosmic-ray transport and acceleration. MHD turbulence can be decomposed into three fundamental modes-fast, slow, and Alfv\'en-each contributing differently to the dynamics of the medium. However, characterizing and separating the energy fractions of these modes was challenging due to the limited 2D information available from observations. To address this difficulty, we use 3D isothermal and multiphase MHD turbulence simulations to examine how mode energy fractions vary under different physical conditions. Overall, we find that the Alfv\'en and slow modes carry comparable kinetic-energy fractions and together dominate the turbulent energy budget in multiphase media, while the fast mode contributes the smallest fraction. Relative to isothermal conditions, multiphase simulations exhibit an enhanced fast-mode energy fraction. We further introduce a machine-learning-based approach that employs a conditional Residual Neural Network to infer these fractions directly from spectroscopic data. The method leverages the fact that the three MHD modes imprint distinct morphological signatures in spectroscopic maps owing to their differing contributions to density and velocity fluctuations. Our model is trained on a suite of isothermal and multiphase simulations covering typical ISM conditions. We demonstrate that our machine learning model can recover the mode fractions from spectroscopic observables, achieving mean relative normalized errors of approximately 0 and standard deviation of 0.01 - 0.02 for seen data and 0.1 - 1.8 for unseen data.