The stability of dense cores near the Serpens South protocluster
Bibliographic record
Abstract
Most stars form in clusters and groups rather than in isolation. We present $\lesssim 5^{\prime\prime}$ angular resolution ($\sim 2000$ au, or 0.01 pc) Very Large Array NH$_3$ (1,1), (2,2), and (3,3) and 1.3 cm continuum emission observations of the dense gas within the Serpens South protocluster and extended filaments to the north and south. We identify 94 dense cores using a dendrogram analysis of the NH$_3$ (1,1) integrated intensity. Gas temperatures $T_K$ and non-thermal linewidths $σ_\mathrm{NT}$ both increase towards the centre of the young stellar cluster, in the dense gas generally and in the cores specifically. We find that most cores (54\%) are super-virial, with gravitationally bound cores located primarily in the filaments. Cores in the protocluster have higher virial parameters by a factor $\sim 1.7$, driven primarily by the increased core $σ_\mathrm{NT}$ values. These cores cannot collapse to form stars unless they accrete additional mass or their core internal motions are reduced. The southern filament shows a significant velocity gradient previously interpreted as mass flow toward the cluster. We find more complex kinematics in the northern filament. We find a strong correlation between $σ_\mathrm{NT}$ and $T_K$, and argue that the enhanced temperatures and non-thermal motions are due to mechanical heating and interaction between the protocluster-driven outflows and the dense gas. Filament-led accretion may also contribute to the increased $σ_\mathrm{NT}$ values. Assuming a constant fraction of core mass ends up in the young stars, future star formation in the Serpens South protocluster will shift to higher masses by a factor $\sim 2$.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.001 | 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".