Massive Compact Disks around FU Orionis–type Young Eruptive Stars Revealed by ALMA
Bibliographic record
Abstract
Abstract The FU Orionis–type objects (FUors) are low-mass pre-main-sequence stars undergoing a temporary but significant increase of mass accretion rate from the circumstellar disk onto the protostar. It is not yet clear what triggers the accretion bursts and whether the disks of FUors are in any way different from the disks of nonbursting young stellar objects. Motivated by this, we conducted a 1.3 mm continuum survey of 10 FUors and FUor-like objects with ALMA, using both the 7 m array and the 12 m array in two different configurations to recover emission at the widest possible range of spatial scales. We detected all targeted sources and several nearby objects as well. To constrain the disk structure, we fit the data with models of increasing complexity from 2D Gaussian to radiative transfer, enabling comparison with other samples modeled in a similar way. The radiative transfer modeling gives disk masses that are significantly larger than what is obtained from the measured millimeter fluxes assuming optically thin emission, suggesting that the FUor disks are optically thick at this wavelength. In comparison with samples of regular class II and class I objects, the disks of FUors are typically a factor of 2.9–4.4 more massive and a factor of 1.5–4.7 smaller in size. A significant fraction of them (65%–70%) may be gravitationally unstable.
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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 | 0.001 | 0.001 |
| 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".