Current Star Formation in the Ophiuchus and Perseus Molecular Clouds: Constraints and Comparisons from Unbiased Submillimeter and Mid‐Infrared Surveys. II.
Bibliographic record
Abstract
We present a census of the population of deeply embedded young stellar objects (YSOs) in the Ophiuchus molecular cloud complex based on a combination of Spitzer Space Telescope mid-infrared data from the "Cores to Disks" (c2d) legacy team and JCMT/SCUBA submillimeter maps from the COMPLETE team. We have applied a method developed for identifying embedded protostars in Perseus to these data sets and in this way construct a relatively unbiased sample of 27 candidate embedded protostars with envelopes more massive than our sensitivity limit (about 0.1 M ☉ ). As in Perseus, the mid-infrared sources are located close to the center of the SCUBA cores and the narrowness of the spatial distribution of mid-infrared sources around the peaks of the SCUBA cores suggests that no significant dispersion of the newly formed YSOs has occurred. Embedded YSOs are found in 35% of the SCUBA cores—fewer than in Perseus (58%). On the other hand the mid-infrared sources in Ophiuchus have less red mid-infrared colors, possibly indicating that they are less embedded. We apply a nearest neighbor surface density algorithm to define the substructure in each of the clouds and calculate characteristic numbers for each subregion—including masses, star formation efficiencies, fraction of embedded sources, etc. Generally the main clusters in Ophiuchus and Perseus (L1688, NGC 1333, and IC 348) are found to have higher star formation efficiencies than small groups such as B1, L1455, and L1448, which on the other hand are completely dominated by deeply embedded protostars. We discuss possible explanations for the differences between the regions in Perseus and Ophiuchus, such as different evolutionary timescales for the YSOs or differences, e.g., in the accretion in the two 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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".