EMBEDDED PROTOSTELLAR DISKS AROUND (SUB-)SOLAR STARS. II. DISK MASSES, SIZES, DENSITIES, TEMPERATURES, AND THE PLANET FORMATION PERSPECTIVE
Bibliographic record
Abstract
We present basic properties of protostellar disks in the embedded phase of star formation (EPSF), which is difficult to probe observationally using available observational facilities. We use numerical hydrodynamics simulations of cloud core collapse and focus on disks formed around stars in the 0.03–1.0 M ☉ mass range. Our obtained disk masses scale near-linearly with the stellar mass. The mean and median disk masses in the Class 0 and I phases ( M mean d,C0 = 0.12 M ☉ , M mdn d,C0 = 0.09 M ☉ and M mean d,CI = 0.18 M ☉ , M mdn d,CI = 0.15 M ☉ , respectively) are greater than those inferred from observations by (at least) a factor of 2–3. We demonstrate that this disagreement may (in part) be caused by the optically thick inner regions of protostellar disks, which do not contribute to millimeter dust flux. We find that disk masses and surface densities start to systematically exceed that of the minimum mass solar nebular for objects with stellar mass as low as M * = 0.05–0.1 M ☉ . Concurrently, disk radii start to grow beyond 100 AU, making gravitational fragmentation in the disk outer regions possible. Large disk masses, surface densities, and sizes suggest that giant planets may start forming as early as in the EPSF, either by means of core accretion (inner disk regions) or direct gravitational instability (outer disk regions), thus breaking a longstanding stereotype that the planet formation process begins in the Class II phase.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".