Milky Way Star Forming Complexes and the Turbulent Motion of the Galaxy’s Molecular Gas
Bibliographic record
Abstract
We analyze Spitzer GLIMPSE, MSX, and WMAP images of the Milky Way to identify 8 micron and free-free sources in the Galaxy. Seventy-two of the eighty-eight WMAP sources have coverage in the GLIMPSE and MSX surveys suitable for identifying massive star forming complexes (SFC). We measure the ionizing luminosity functions of the SFCs and study their role in the turbulent motion of the Galaxy's molecular gas. We find a total Galactic free-free flux f_{\nu} = 46177.6 Jy; the 72 WMAP sources with full 8 micron coverage account for 34263.5 Jy (~75%), with both measurements made at \nu=94GHz (W band). We find a total of 280 SFCs, of which 168 have unique kinematic distances and free-free luminosities. We use a simple model for the radial distribution of star formation to estimate the free-free and ionizing luminosity for the sources lacking distance determinations. The total dust-corrected ionizing luminosity is Q = 2.9 \pm 0.5 x 10^53 photons s^-1, which implies a galactic star formation rate of 1.2 \pm 0.2 M_{\sun} yr^-1. We present the (ionizing) luminosity function of the SFCs, and show that 24 sources emit half the ionizing luminosity of the Galaxy. The SFCs appear as bubbles in GLIMPSE or MSX images; the radial velocities associated with the bubble walls allow us to infer the expansion velocity of the bubbles. We calculate the kinetic luminosity of the bubble expansion and compare it to the turbulent luminosity of the inner molecular disk. SFCs emitting 80% of the total galactic free-free luminosity produce a kinetic luminosity equal to 65% of the turbulent luminosity in the inner molecular disk. This suggests that the expansion of the bubbles is a major driver of the turbulent motion of the inner Milky Way molecular gas.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".