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Record W2597206547 · doi:10.3847/1538-3881/aa5ff8

Widening of Protostellar Outflows: An Infrared Outflow Survey in Low-luminosity Objects

2017· article· en· W2597206547 on OpenAlexaboutno aff

Bibliographic record

VenueThe Astronomical Journal · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Star Formation Studies
Canadian institutionsnot available
Fundersnot available
KeywordsOutflowProtostarInfraredLuminosityRadiative transferYoung stellar object

Abstract

fetched live from OpenAlex

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 K s 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.457

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.027
GPT teacher head0.273
Teacher spread0.246 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations25
Published2017
Admission routes1
Has abstractyes

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