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  • An Exploration of the Statistical Signatures of Stellar Feedback

    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 winds interacting with molecular clouds which vary the stellar mass-loss rates and magnetic field strength. 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, such as principle component analysis, velocity component spectrum, and dendrograms, are sensitive to changes in stellar mass-loss rates and/or magnetic field strength. This demonstrates that stellar feedback influences molecular cloud turbulence and can be identified and quantified observationally using such statistics.


    Turbulence in the interstellar medium is ubiquitous and self-similar across many orders of magnitude (Brandenburg et al., 2013). Within molecular clouds, turbulence appears to play an essential role in the star formation process, regulating the efficiency at which stars form, seeding filaments and over-densities, and even potentially setting the stellar initial mass function (Padoan et al., 2014; Offner et al., 2014). While the presence of supersonic motions is readily verified and has been studied using molecular spectral lines for several decades (Larson, 1981), the origin, energy injection scale, means of sustenance, and rate of dissipation remain debated. Moreover, molecular clouds display significant variation in bulk properties, ongoing star formation, and morphology. Consequently, it seems highly likely that these differences impact the turbulent properties of the gas and leave signatures — but if so they are difficult to identify observationally.

    Detailed study of the turbulence within molecular clouds is confounded by a variety of factors including observational resolution, projection effects, complex gas chemistry, and variable local conditions (e.g., Beaumont et al., 2013, and references therein). Any one molecular tracer only samples a limited set of gas densities and scales, so that reconstructing cloud kinematics reliably involves assembling a variety of tracers across different densities and scales (e.g. Gaches et al., 2015). Many studies of cloud structure instead rely on a single gas tracer like CO, which is bright and exhibits widespread emission that reflects the underlying H\(_2\) distribution (Bolatto et al., 2013; Heyer et al., 2015). Connecting such emission data to underlying turbulence and bulk cloud properties, however, is non-trivial. A variety of statistics have been proposed throughout the literature to characterize spectral data cubes and distill the complex emission information into more manageable 1D or 2D forms (e.g., Heyer et al., 1997; Rosolowsky et al., 1999; Rosolowsky et al., 2008; Burkhart et al., 2009). However, in most cases, the utility of the statistic and its interpretation is not well constrained.

    Numerical simulations, which supply full 6-D information \((x,y,z, v_x, v_y, v_z)\), provide a means to study turbulence and constrain cloud properties. Prior studies have