A FIRST LOOK AT THE AURIGA-CALIFORNIA GIANT MOLECULAR CLOUD WITH<i>HERSCHEL</i>AND THE CSO: CENSUS OF THE YOUNG STELLAR OBJECTS AND THE DENSE GAS
Bibliographic record
Abstract
We have mapped the Auriga/California molecular cloud with the Herschel PACS and SPIRE cameras and the Bolocam 1.1 mm camera on the Caltech Submillimeter Observatory with the eventual goal of quantifying the star formation and cloud structure in this giant molecular cloud (GMC) that is comparable in size and mass to the Orion GMC, but which appears to be forming far fewer stars. We have tabulated 60 compact 70/160 μm sources that are likely pre-main-sequence objects and correlated those with Spitzer and WISE mid-IR sources. At 1.1 mm, we find 18 cold, compact sources and discuss their properties. The most important result from this part of our study is that we find a modest number of additional compact young objects beyond those identified at shorter wavelengths with Spitzer . We also describe the dust column density and temperature structure derived from our photometric maps. The column density peaks at a few × 10 22 cm −2 ( N H2 ) and is distributed in a clear filamentary structure along which nearly all of the pre-main-sequence objects are found. We compare the young stellar object surface density to the gas column density and find a strong nonlinear correlation between them. The dust temperature in the densest parts of the filaments drops to ∼10 K from values ∼14–15 K in the low-density parts of the cloud. We also derive the cumulative mass fraction and probability density function of material in the cloud, which we compare with similar data on other star-forming clouds.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".