THE LUMINOSITIES OF PROTOSTARS IN THE<i>SPITZER</i>c2d AND GOULD BELT LEGACY CLOUDS
Bibliographic record
Abstract
Motivated by the long-standing "luminosity problem" in low-mass star formation whereby protostars are underluminous compared to theoretical expectations, we identify 230 protostars in 18 molecular clouds observed by two Spitzer Space Telescope Legacy surveys of nearby star-forming regions. We compile complete spectral energy distributions, calculate L bol for each source, and study the protostellar luminosity distribution. This distribution extends over three orders of magnitude, from 0.01 L ☉ to 69 L ☉ , and has a mean and median of 4.3 L ☉ and 1.3 L ☉ , respectively. The distributions are very similar for Class 0 and Class I sources except for an excess of low luminosity ( L bol ≲ 0.5 L ☉ ) Class I sources compared to Class 0. 100 out of the 230 protostars (43%) lack any available data in the far-infrared and submillimeter (70 μm <λ < 850 μm) and have L bol underestimated by factors of 2.5 on average, and up to factors of 8–10 in extreme cases. Correcting these underestimates for each source individually once additional data becomes available will likely increase both the mean and median of the sample by 35%–40%. We discuss and compare our results to several recent theoretical studies of protostellar luminosities and show that our new results do not invalidate the conclusions of any of these studies. As these studies demonstrate that there is more than one plausible accretion scenario that can match observations, future attention is clearly needed. The better statistics provided by our increased data set should aid such future work.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".