[Fe II] 1.64 μm FEATURES OF JETS AND OUTFLOWS FROM YOUNG STELLAR OBJECTS IN THE CARINA NEBULA
Bibliographic record
Abstract
We present [Fe ii ] 1.64 μm imaging observations for jets and outflows from young stellar objects (YSOs) over the northern part (∼24' × 45') of the Carina Nebula, a massive star-forming region. The observations were performed with IRIS2 of the Anglo-Australian Telescope and the seeing was ∼1 5 ± 0 5. Eleven jet and outflow features are detected at eight different regions and are termed ionized Fe objects (IFOs). One Herbig–Haro candidate that was missed in Hubble Space Telescope Hα observations is newly identified as HHc-16, referring to our [Fe ii ] images. IFOs have knotty or longish shapes, and the detection rate of IFOs against previously identified YSOs is 1.4%, which should be treated as a lower limit. Four IFOs show anti-correlated peak intensities in [Fe ii ] and Hα, where the ratio I ([Fe ii ])/ I (Hα) is higher for longish IFOs than for knotty IFOs. We estimate the outflow mass loss rate from the [Fe ii ] flux using two different methods. The jet-driving objects are identified for three IFOs (IFO-2, -4, and -7) for which we study the relations between the outflow mass loss rate and the YSO physical parameters from the radiative transfer model fitting. The ratios of the outflow mass loss rate over the disk accretion rate for IFO-4 and -7 are consistent with the previously reported values (10 −2 –10 +1 ), while the ratio is higher for IFO-2. This excess may result from underestimating the disk accretion rate. The jet-driving objects are likely to be low- or intermediate-mass stars. Other YSO physical parameters, such as luminosity and age, show reasonable relations or trends.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".