So Long Kolmogorov: The Forward and Backward Turbulence Cascades in a Supernovae-driven, Multiphase Interstellar Medium
Bibliographic record
Abstract
Abstract The interstellar medium (ISM) of disk galaxies is turbulent, and yet the fundamental nature of ISM turbulence, the energy cascade, is not understood in detail. In this study, we use high-resolution simulations of a hydrodynamical, gravitationally stratified, supernovae (SNe) driven, multiphase ISM to probe the nature of a galactic turbulence cascade. Through the use of velocity flux transfer functions split into interactions between compressible u c and incompressible u s modes, we show that there exists a large-to-small-scale cascade in both u c and u s when mediated by an additional u s mode. But the u s cascade is highly nonlocal. Moreover, there is a u c -mediated component of the u s cascade that proceeds in the opposite direction—an inverse cascade from small-to-large scales. The cascade feeds flux into scales well beyond the scale height, energizing the winds and fueling the direct cascades. Both the strongly nonlocal and the inverse u s cascades happen on scales that have a power-law u s energy spectrum, highlighting how degenerate the spectrum is to the true underlying physical processes. We directly show that the inverse cascade comes from u s modes interacting with expanding SNe remnants (SNRs) and that u s modes are generated to leading order via baroclinic, highly corrugated cooling layers between warm ( T ≲ 10 4 K) and hot ( T ≫ 10 4 K) gas in these SNRs. Finally, we outline a complete phenomenology for SNe-driven turbulence in a galactic disk, estimate a 10 −16 G Biermann field generated from SNR cooling layers, and highlight the strong deviations that SNe-driven turbulence has from the conventional Kolmogorov model.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".