The Planck Submillimeter Properties of Galactic High-mass Star-forming Regions: Dust Temperatures, Luminosities, Masses, and Star Formation Efficiency
Bibliographic record
Abstract
Abstract Massive star formation occurs in the interior of giant molecular clouds and proceeds through many stages. In this work, we focus on massive young stellar objects (MYSOs) and ultracompact H ii regions (UCH ii), where the former are enshrouded in dense envelopes of dust and gas, the latter of which has begun dispersing. By selecting a complete sample of MYSOs and UCH ii from the Red MSX Source (RMS) survey database, we combine Planck and IRAS data and build their spectral energy distributions. With these, we estimate the physical properties (dust temperatures, mass, luminosity) of the sample. Because the RMS database provides unique solar distances, it also allows the instantaneous star formation efficiency (SFE) to be investigated as a function of Galactocentric radius. We find that the SFE increases between 2 and 4.5 kpc, where it reaches a peak, likely in correspondence with the accumulation of molecular material at the end of the Galactic bar. It then stays approximately constant up to 9 kpc, after which it linearly declines, in agreement with predictions from extragalactic studies. This behavior suggests the presence of a significant amount of undetected molecular gas at R G > 8 kpc. Finally, we present diagnostic colors that can be used to identify sites of massive star formation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".