ELT-METIS Imaging Simulations for Disks and Envelopes Associated with FU Ori-type Objects
Bibliographic record
Abstract
Abstract We investigate the detectability of extended mid-infrared (MIR) emission associated with FU-Ori type objects (FUors) using the Mid-infrared ELT Imager and Spectrograph (METIS) coronagraphs on the 39 m Extremely Large Telescope. The imaging simulations were made for three representative filters (λ = 3.8, 4.8, and 11.3 μm) of the METIS instrument. We demonstrate that the detectability of the extended MIR emission using these coronagraphs is highly dependent on the uncertain nature of the central FUor and its circumstellar environment in various contexts. These contexts are: (A) whether the central radiation source is either a flat self-luminous accretion disk or a star at near-infrared (NIR) wavelengths, (B) the size of the accretion disk for the bright central MIR emission at milliarcsecond scales, (C) whether the extended emission is due to either an optically thick disk or an optically thin envelope, and (D) dust grain models. Observations at λ = 3.8 μm will allow us to detect the extended emission in many cases, while the number of cases with detection may significantly decrease toward longer wavelengths due to the fainter nature of the extended emission and high thermal background noise. In some cases, the presence of a binary companion can significantly hamper detections of the extended MIR emission. NIR and MIR imaging observations at existing 8 m class telescopes, prior to the METIS observations, will be useful for (1) reducing the many model uncertainties and (2) searching for binary companions associated with FUors, therefore determining the best observing strategy using METIS.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".