The H i-to-H₂ Transition in a Turbulent Medium

Abstract

We study the effect of density fluctuations induced by turbulence on the HI/H 2 structure in photodissociation regions (PDRs) both analytically and numerically. We perform magnetohydrodynamic numerical simulations for both subsonic and supersonic turbulent gas, and chemical HI/H 2 balance calculations. We derive atomicto-molecular density profiles and the HI column density probability density function (PDF) assuming chemical equilibrium. We find that while the HI/H 2 density profiles are strongly perturbed in turbulent gas, the mean HI column density is well approximated by the uniform-density analytic formula of Sternberg et al. (2014). The PDF width depends on (a) the radiation intensity to mean density ratio, (b) the sonic Mach number and (c) the turbulence decorrelation scale, or driving scale. We derive an analytic model for the HI PDF and demonstrate how our model, combined with 21 cm observations, can be used to constrain the Mach number and driving scale of turbulent gas. As an example, we apply our model to observations of HI in the Perseus molecular cloud. We show that a narrow observed HI PDF may imply small scale decorrelation, pointing to the potential importance of subcloud-scale turbulence driving.

Spatial power spectra of dust across the Local Group: No constraint on disc scale height

“…We also note that power spectrum breaks are not commonly found on smaller scales within nearby Milky Way molecular clouds (< 20 pc). Power spectrum studies of the Perseus molecular cloud include scales where stellar feedback provides sufficient energy to drive turbulence (Padoan et al 2009;Arce et al 2011) but do not find a power spectrum break, despite results from alternative methods, like the probability distribution function (PDF), that suggest small scale driving should be dominant (Bialy et al 2017).…”

Do Androids Dream of Magnetic Fields? Using Neural Networks to Interpret the Turbulent Interstellar Medium

“…Magnetohydrodyanmic (MHD) turbulence is now part of the established paradigm of the interstellar medium (ISM) of galaxies and can influence the behavior of gas and dust over scales ranging from beyond a kiloparsec to below AU (Armstrong et al 1995;Elmegreen & Scalo 2004;Burkhart 2014). MHD turbulence is understood to be important for fundamental astrophysical processes including star formation, galactic pressure support, the transport of heat and metals, formation of molecular hydrogen, the acceleration and diffusion of cosmic rays and the structure of magnetic field lines (Lazarian & Vishniac 1999;Mac Low & Klessen 2004;Lazarian 2006;Yan 2009;Burkhart et al 2010;Burkhart & Lazarian 2012;Federrath & Klessen 2012;Bialy et al 2017;Pingel et al 2018).…”

TurbuStat: Turbulence Statistics in Python

“…-The most commonly-used analysis technique to describe turbulent properties from observational data products is the PDF. Extensive work on PDFs from simulations (Vazquez-Semadeni 1994;Ostriker et al 2001;Kowal et al 2007;Federrath et al 2008;Burkhart et al 2009;Burkhart et al 2017, e.g.,) and observations (e.g., Miesch & Scalo 1995;Burkhart et al 2010;Lombardi et al 2015;Imara & Burkhart 2016;Bialy et al 2017) has provided a solid framework connecting the PDF to turbulent properties. The TurbuStat PDF implementation was written to emphasize flexibility in treatment and modelling of PDFs.…”

“…Turbulence in the ISM may be driven by multiple energy injection sources on different scales (McKee & Ostriker 2007;Krumholz et al 2014;Chepurnov et al 2015;Krumholz et al 2018) and is affected by variations in magnetic field strength and orientation (Goldreich & Sridhar 1995;Burkhart et al 2009;Meyer et al 2014;Burkhart et al 2015b;Hull et al 2017). There are additional physical effects at work, including phase transitions that affect thermodynamic properties and gravitational collapse within molecular clouds (Bialy et al 2017;Hill et al 2018).…”