The census of dense cores in the Serpens region from the <i>Herschel</i> Gould Belt Survey
Bibliographic record
Abstract
ABSTRACT The Herschel Gould Belt survey mapped the nearby (d < 500 pc) star-forming regions to understand better how the prestellar phase influences the star formation process. Here, we report a complete census of dense cores in a ∼15 deg2 area of the Serpens star-forming region located between d ∼ 420 and 484 pc. The PACS and SPIRE cameras imaged this cloud from 70 to 500 μm. With the multiwavelength source extraction algorithm getsources, we extract 833 sources, of which 709 are starless cores and 124 are candidate protostellar cores. We obtain temperatures and masses for all the sample, classifying the starless cores in 604 prestellar cores and 105 unbound cores. Our census of sources is $80{{\ \rm per\ cent}}$ complete for M > 0.8 M⊙ overall. We produce the core mass function (CMF) and compare it with the initial mass function (IMF). The prestellar CMF is consistent with lognormal trend up to ∼2 M⊙, after which it follows a power law with slope of −2.05 ± 0.34. The tail of its CMF is steeper but still compatible with the IMF for the region we studied in this work. We also extract the filaments network of the Serpens region, finding that $81{{\ \rm per\ cent}}$ of prestellar cores lie on filamentary structures. The spatial association between cores and filamentary structure supports the paradigm, suggested by other Herschel observations, that prestellar cores mostly form on filaments. Serpens is confirmed to be a young, low-mass and active star-forming region.
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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 | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".