Zum Hauptinhalt springen

Quicklinks

SUPERNOVA DRIVEN SUPERSONIC TURBULENCE IN MOLECULAR CLOUDS

The turbulence within molecular clouds has been observed to be supersonic in nature, and in every stage of cloud evolution. Supersonic turbulence decays over the lifetime of a molecular cloud, begging the question what is driving it. One of the possible driving factors is supernovae, which release the sufficient amount of energy to drive turbulence. When the expanding supernova driven shell hits a dense molecular cloud, its kinetic energy is transformed into turbulent energy. 
We produce synthetic observations of the SILCC zoom-in simulations (presented in Seifried et. al 2017) using the radiative transfer code RADMC-3D. In these simulation we explode six supernovae at different positions (one in each of the Cartesian direction) at a distance of 25 pc of the center of the cloud. A supernova occurs every 300 kyr and we observe the molecular cloud over a time span of 2.5 Myr. 
The figure shows the time evolution of the intensity weighted velocity dispersion with (dashed) and without (solid) supernova explosions for the three different lines-of-sight. Each vertical black line represents the time when a supernova occurs. We can clearly see a sharp increase in velocity dispersion when a supernova occurs, which is followed by a fast decay. This shows that supernovae are able to drive turbulence, but are able to do so only for short timescales. Moreover, the effect is lessened for dense gas tracers (i.e. 13CO and C18O), indicating that it is increasingly difficult to drive turbulence in denser regions of a molecular cloud. 

Evolution of the velocity dispersion of the modelled molecular cloud subjected to SNe (dashed lines) and without SNe (solid lines). The velocity dispersion is measured in 12CO (blue lines), 13CO (orange lines) and C18O (green lines). The black vertical lines indicate the time when the SNe occur. The panels from right to left correspond to the sightlines along directions x, y and z, respectively.

Monthly Highlights

September 2026

Multidimensional finite volume methods with constrained transport for MHD simulations of the ISM (Mervin Yap)

July 2026

A Probabilistic Machine Learning Approach to Emulate Chemistry in Astrophysical Simulations (Lennart Buhlmann & Felix Rauprich)

June 2026

How reliably can core masses be inferred from ALMA continuum emission? (Birka Zimmermann)

May 2026

Following Tracer Particles to Identify Filaments in Star-Forming Cores with HDBSCAN (Nuray Ortaköse)

April 2026

Estimating ionization fractions in SILCC simulations (Lennart Buhlmann)

March 2026

1D protostellar disk sub-grid model for star formation 3D MHD simulations (Anaïs Pauchet)

January 2026

Protostellar Outflows: From Simulations to Synthetic Observations (Taishi Ushirogi)