An Updated Modular Set of Synthetic Spectral Energy Distributions for Young Stellar Objects
Bibliographic record
Abstract
Abstract Measured properties of young stellar objects (YSOs) are key tools for research into pre-main-sequence stellar evolution. YSO properties are commonly measured by comparing observed radiation to existing grids of template YSO spectral energy distributions (SEDs) calculated by radiative transfer. These grids are often sampled and constructed using simple models of mass assembly/accretion over time. However, because we do not yet have a complete theory of star formation, the choice of model sets the tracked parameters and range of allowed values. By construction, then, the assumed model limits the measurements that can be made using the grid. Radiative transfer models not constrained by specific accretion histories would enable assessment of a wider range of theories. We present an updated version of the Robitaille set (2017) of YSO SEDs, a collection of models with no assumed evolutionary theory. We outline our newly calculated properties: envelope mass, weighted-average dust temperature, disk stability, and circumstellar A V. We also convolve the SEDs with new filters, including JWST, and provide users the ability to perform additional convolutions. We find a correlation between the average temperature and millimeter-wavelength brightness of optically thin dust in our models and discuss its ramifications for mass measurements of pre- and protostellar cores. We also compare the positions of YSOs of different observational classes and evolutionary stages in IR color space and use our models to quantify the extent to which class and stage may be confused due to observational effects. Our updated models are released to the public.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".