Widening of Protostellar Outflows: An Infrared Outflow Survey in Low-luminosity Objects
Bibliographic record
Abstract
Abstract We present an outflow survey toward 20 low-luminosity objects (LLOs), namely, protostars with an internal luminosity lower than 0.2 . Although a number of studies have reported the properties of individual LLOs, the reasons for their low luminosity remain uncertain. To answer this question, we need to know the evolutionary status of LLOs. Protostellar outflows are found to widen as their parent cores evolve, and therefore the outflow opening angle could be used as an evolutionary indicator. The infrared scattered light escapes out through the outflow cavity and highlights the cavity wall, giving us the opportunity to measure the outflow opening angle. Using the Canada–France–Hawaii Telescope, we detected outflows toward eight LLOs out of 20 at Ks band, and based on archival Spitzer IRAC1 images, we added four outflow-driving sources from the remaining 12 sources. By fitting these images with radiative transfer models, we derive the outflow opening angles and inclination angles. To study the widening of outflow cavities, we compare our sample with the young stellar objects from Arce & Sargent and Velusamy et al. in a plot of opening angle versus bolometric temperature taken as an evolutionary indicator. Our LLO targets match well the trend of increasing opening angle with bolometric temperature reported by Arce & Sargent and are broadly consistent with that reported by Velusamy et al., suggesting that the opening angle could be a good evolutionary indicator for LLOs. Accordingly, we conclude that at least 40% of the outflow-driving LLOs in our sample are young Class 0 objects.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".