Investigating the asymmetry of young stellar outflows: Combined MUSE-X-shooter study of the Th 28 jet
Bibliographic record
Abstract
Context. Characterising stellar jet asymmetries is key to setting robust constraints on jet launching models and improving our understanding of the underlying mechanisms behind jet launching. Aims. We aim to characterise the asymmetric properties of the bipolar jet coming from the Classical T Tauri Star Th 28. Methods. We combined data from integral field spectroscopy with VLT/MUSE and high-resolution spectra from VLT/X-shooter to map the optical emission line ratios in both jet lobes. We carried out a diagnostic analysis of these ratios to compare the density, electron temperature, and ionisation fraction within both lobes. The mass accretion rate was derived from the emission lines at the source and compared with the mass outflow rate derived for both lobes, using the estimated densities and measured [O I]λ6300 and [S II]λ6731 luminosities. Results. The blue-shifted jet exhibits a significantly higher electron temperature and moderately higher ionisation fraction than the red-shifted jet. In contrast to previous studies, we also estimated higher densities, denoted as nH , in the blue-shifted jet by a factor of ~2. These asymmetries are traced to within 1″ (160 au) of the source in the line ratio maps. We find Ṁacc = 2.4 × 10−7 M⊙ yr−1, with an estimated obscuration factor of ~54 due to grey scattering around the star. Estimated values of Ṁout range between 0.66 and 13.7 × 10−9 M⊙ yr−1 in the blue-shifted jet and 5–9 × 10−9 M⊙ yr−1 in the red-shifted jet. Conclusions. The emission line maps and diagnostic results suggest that the jet asymmetries originate close to the source and are likely to be intrinsic to the jet. Furthermore, the combined dataset offers access to a broad array of accretion tracers. In turn, this enables a more accurate estimation of the mass accretion rate, revealing a value of Ṁacc that is higher by a factor >350 than would otherwise be determined.
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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 | 0.000 |
| 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".