A survey for ground-state OH masers towards a sample of Herbig–Haro objects
Bibliographic record
Abstract
Herbig–Haro objects are regions of shocked gas and dust which are produced when collimated outflows from a protostar interact with the surrounding dense gas. They have many similarities to supernova remnants which are interacting with molecular clouds. 1720-MHz OH masers have been identified towards a number of interacting supernova remnants. Observations and models indicate that these masers are shock excited and are produced behind C-type shocks. If conditions behind the shock fronts of Herbig–Haro objects are similarly able to support 1720-MHz OH masers they could be a useful diagnostic tool for star formation. We therefore searched for 1720-MHz OH maser emission towards a sample of 97 Herbig–Haro objects using the Green Bank Radio Telescope. We detected 1720-MHz OH lines in emission in 17 of them, but neither their spectral signature nor follow-up observations with the Very Large Array showed any conclusive evidence of maser emission. We conclude that the emission detected from our single-dish observations must be extended and most likely originates from thermal or quasi-thermal excitation processes. We also investigated the properties of Herbig–Haro shocks more closely and conclude that despite the overall similarities to supernova remnants, the conditions required for maser emission, in particular a sufficient velocity-coherent column density, are not likely to occur in Herbig–Haro 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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 0.001 |
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".