Isotropically Driven versus Outflow Driven Turbulence: Observational Consequences for Molecular Clouds

TL;DR

This study compares isotropically driven and outflow driven turbulence in molecular clouds, revealing how outflows influence turbulence spectra and density structures, and assessing PCA's effectiveness in identifying turbulence driving scales.

Contribution

It combines high-resolution simulations of outflow and isotropic turbulence, showing how outflows alter spectral slopes and density structures, and evaluates PCA's bias in turbulence analysis.

Findings

Outflows create a knee in the velocity power spectrum at the outflow scale.

Outflows flatten the large-scale density spectrum, reducing large-scale structures.

PCA tends to bias toward large-scale velocity features, especially in outflow-driven turbulence.

Abstract

Feedback from protostellar outflows can influence the nature of turbulence in star forming regions even if they are not the primary source of velocity dispersion for all scales of molecular clouds. For the rate and power expected in star forming regions, we previously (Carroll et al. 2009) demonstrated that outflows could drive supersonic turbulence at levels consistent with the scaling relations from Matzner 2007 although with a steeper velocity power spectrum than expected for an isotropically driven supersonic turbulent cascade. Here we perform higher resolution simulations and combine simulations of outflow driven turbulence with those of isotropically forced turbulence. We find that the presence of outflows within an ambient isotropically driven turbulent environment produces a knee in the velocity power spectrum at the outflow scale and a steeper slope at sub-outflow scales than for a purely isotropically forced case. We also find that the presence of outflows flattens the density spectrum at large scales effectively reducing the formation of large scale turbulent density structures. These effects are qualitatively independent of resolution. We have also carried out Principal Component Analysis (PCA) for synthetic data from our simulations. We find that PCA as a tool for identifying the driving scale of turbulence has a misleading bias toward low amplitude large scale velocity structures even when they are not necessarily the dominant energy containing scales. This bias is absent for isotropically forced turbulence but manifests strongly for collimated outflow driven turbulence.