Shock interactions, turbulence and the origin of the stellar mass spectrum
Bibliographic record
Abstract
Supersonic turbulence is an essential element in understanding how structure within interstellar gas is created and shaped. In the context of star formation, many computational studies show that the mass spectrum of density and velocity fluctuations within dense clouds, as well as the distribution of their angular momenta, trace their origin to the statistical and physical properties of gas that is lashed with shock waves. In this paper, we review the observations, simulations and theories of how turbulent-like processes can account for the structures we see in molecular clouds. We then compare traditional ideas of supersonic turbulence with a simpler physical model involving the effects of multiple shock waves and their interactions in the interstellar medium. Planar intersecting shock waves produce dense filaments and generate vortex sheets that are essential to create the broad range of density and velocity structure in clouds. As an example, the lower-mass behaviour of the stellar initial mass function can be traced to the tendency of a collection of shock waves to build up a lognormal density distribution (or column density). Vorticity--which is essential to produce velocity structure over a very broad range of length scales in shocked clouds--can also be generated by the passage of curved shocks or intersecting planar shocks through such media. Two major additional physical forces affect the structure of star-forming gas--gravity and feedback processes from young stars. Both of these can produce power-law tails at the high-mass end of the initial mass function.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".