DENSE CORES IN PERSEUS: THE INFLUENCE OF STELLAR CONTENT AND CLUSTER ENVIRONMENT
Bibliographic record
Abstract
We present the chemistry, temperature, and dynamical state of a sample of 193 dense cores or core candidates in the Perseus Molecular cloud and compare the properties of cores associated with young stars and clusters with those which are not. The combination of our NH3 and CCS observations with previous millimeter, submillimeter, and Spitzer data available for this cloud enables us both to determine core properties precisely and to accurately classify cores as starless or protostellar. The properties of cores in different cluster environments and before-and-after star formation provide important constraints on simulations of star formation, particularly under the paradigm that the essence of star formation is set by the turbulent formation of prestellar cores. We separate the influence of stellar content from that of the cluster environment and find that cores within clusters have (1) higher kinetic temperatures (12.9 K versus 10.8 K) and, (2) lower fractional abundances of CCS \\((0.6 × 10^{–9}\\) versus \\(2.0 × 10^{–9})\\) and \\(NH_3 (1.2 × 10^{–8}\\) versus \\(2.9 × 10^{–8})\\). Cores associated with protostars have (1) slightly higher kinetic temperatures (11.9 K versus 10.6 K), (2) higher NH3 excitation temperatures (7.4 K versus 6.1 K), (3) are at higher column density \\((1.2 × 10^{22} cm^{–2}\\) versus \\(0.6 × 10^{22} cm^{–2})\\), have (4) slightly more nonthermal/turbulent \\(NH_3\\) line widths \\((0.14 km \\ s^{–1}\\) versus \\(0.11 km \\ s^{–1} FWHM)\\), have (5) higher masses \\((1.5 M \\odot\\) versus \\(1.0 M \\odot)\\), and have (6) lower fractional abundance of CCS \\((1.4 × 10^{–9}\\) versus \\(2.4 × 10^{–9})\\). All values are medians. We find that neither cluster environment nor protostellar content makes a significant difference to the dynamical state of cores as estimated by the virial parameter—most cores in each category are gravitationally bound. Only the high precision of our measurements and the size of our sample make such distinctions possible. Overall, cluster environment and protostellar content have a smaller influence on the properties of the cores than is typically assumed, and the variation within categories is larger than the differences between categories.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".