An Exploration of the Statistical Signatures of Stellar Feedback

Abstract

All molecular clouds are observed to be turbulent, but the origin, means of sustenance, and evolution of the turbulence remain debated. One possibility is that stellar feedback injects enough energy into the cloud to drive observed motions on parsec scales. Recent numerical studies of molecular clouds have found that feedback from stars, such as protostellar outflows and winds, injects energy and impacts turbulence. We expand upon these studies by analyzing magnetohydrodynamic simulations of molecular clouds, including stellar winds, with a range of stellar mass-loss rates and magnetic field strengths. We generate synthetic 12 CO(1-0) maps assuming that the simulations are at the distance of the nearby Perseus molecular cloud. By comparing the outputs from different initial conditions and evolutionary times, we identify differences in the synthetic observations and characterize these using common astrostatistics. We quantify the different statistical responses using a variety of metrics proposed in the literature. We find that multiple astrostatistics, including principal component analysis, the spectral correlation function and the velocity coordinate spectrum, are sensitive to changes in stellar mass-loss rates and/or time evolution. A few statistics, including the Cramer statistic and velocity coordinate spectrum, are sensitive to the magnetic field strength. These findings demonstrate that stellar feedback influences molecular cloud turbulence and can be identified and quantified observationally using such statistics.

Impact of Protostellar Outflows on Turbulence and Star Formation Efficiency in Magnetized Dense Cores

“…Simulations show outflows can produce differences in slope or features in the power spectrum (e.g., Carroll et al 2009Carroll et al , 2010. Stronger energetic feedback from winds, which evacuate molecular material on parsec scales, may produce even more significant changes Boyden et al 2016). However, detecting and characterizing the influence of feedback in observational spectral data is challenging and such studies have focused on larger scales than those studied in this work (e.g., Swift & Welch 2008).…”

TurbuStat: Turbulence Statistics in Python

“…TurbuStat (v1.0 Koch et al 2019) has implementations of 14 literature methods 3 that recover properties related to turbulence from observational data. We briefly describe the methods and relevant literature here (also see descriptions in Boyden et al 2016;Koch et al 2017;Boyden et al 2018), and note that the package documentation contains thorough explanations and code examples.…”

“…The methods implemented in Tur-buStat measure properties of the underlying physics in observational data; these distance metrics are one approach for quantifying the difference in their physical properties. In previous works, we have used these distance metrics to find which methods are sensitive to different input parameters in sets of simulations (Yeremi et al 2014;Boyden et al 2016;Koch et al 2017;Boyden et al 2018). We refer readers to Koch et al (2017) for a full description of the distance metrics.…”

“…We have developed TurbuStat, a publiclyavailable Python package that implements fourteen observational diagnostics of ISM turbulence described in the literature. TurbuStat provides a common framework for running and comparing turbulence diagnostics, including comparisons between simulations and observations (e.g., Boyden et al 2016;Koch et al 2017;Boyden et al 2018;Haworth et al 2018). The use of some techniques has been limited by the lack of a publicly-available implementation.…”

“…We note that this number differs fromBoyden et al (2016Boyden et al ( , 2018 since (i) the Tsallis statistic was not used and (ii) multiple outputs of dendrograms and statistical moments were counted as individual methods.…”