Diagnosing FU Ori-like Sources: The Parameter Space of Viscously Heated Disks in the Optical and Near-infrared
Bibliographic record
Abstract
Abstract FU Ori-type objects (FUors) are decades-long outbursts of accretion onto young stars that are strong enough to viscously heat disks so that the disk outshines the central star. We construct models for FUor objects by calculating emission components from a steady-state viscous accretion disk, a passively-heated dusty disk, magnetospheric accretion columns, and the stellar photosphere. We explore the parameter space of the accretion rate M ̇ and stellar mass M * to investigate implications on the optical and near-infrared spectral energy distribution and spectral lines. The models are validated by fitting to multiwavelength photometry of three confirmed FUor objects, FU Ori, V883 Ori, and HBC 722, and then comparing the predicted spectra to the observed optical and infrared spectra. The brightness ratio between the viscous disk and the stellar photosphere, η, provides an important guide for identifying viscous accretion disks, with η = 1 (“transition line”) and η = 5 (“sufficient dominance line”) marking turning points in diagnostics, evaluated here in the near-infrared. These turning points indicate the emergence and complete development of FUor-characteristic strong CO absorption, weak metallic absorption, the triangular spectral continuum shape in the H band, and location in color–magnitude diagrams. Lower M * and higher M ̇ imply larger η; for M * = 0.3 M ⊙, η = 1 corresponds to M ̇ = 2 × 10 − 7 M ⊙ yr−1 and η = 5 to M ̇ = 6 × 10 − 7 M ⊙ yr−1. The “sufficient dominance line” also coincides with the expected accretion rate where accreting material directly reaches the star. We discuss implications of the models on extinction diagnostics, FUor brightening timescales, viscous disks during initial protostellar growth, and eruptive young stellar objects associated with FUors.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".