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Record W2890124778 · doi:10.1051/0004-6361/201833245

Dust spectrum and polarisation at 850 <i>μ</i>m in the massive IRDC G035.39-00.33

2018· article· en· W2890124778 on OpenAlexfundno aff
M. Juvela, V. Guillet, Tie Liu, I. Ristorcelli, V.-M. Pelkonen, Dana Alina, L. Bronfman, David Eden, Kee Tae Kim, Patrick M. Koch, Woojin Kwon, Chang Won Lee, J. Malinen, Elisabetta R. Micelotta, J. Montillaud, Mark G. Rawlings, Patricio Sanhueza, Archana Soam, A. Traficante, N. Ysard, Chuan-Peng Zhang

Bibliographic record

VenueAstronomy and Astrophysics · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Star Formation Studies
Canadian institutionsnot available
FundersComisión Nacional de Investigación Científica y TecnológicaCanadian Space AgencyEast Asian Core Observatories AssociationNational Astronomical Observatory of JapanNational Aeronautics and Space AdministrationMinistry of Education, Science and TechnologyKorea Astronomy and Space Science InstituteNational Research Foundation of KoreaCentre National d’Etudes SpatialesCentre National de la Recherche ScientifiqueChinese Academy of SciencesAcademy of FinlandScience and Technology Facilities CouncilNational Research FoundationEuropean Space AgencyCalifornia Institute of TechnologyAcademia Sinica
KeywordsPhysicsAstrophysicsOpacityRadiative transferMillimeterContext (archaeology)PlanckProtein filamentSpectral indexOptical depthSpectral lineOpticsAerosolAstronomyChemistry

Abstract

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Context. The sub-millimetre polarisation of dust emission from star-forming clouds carries information on grain properties and on the effects that magnetic fields have on cloud evolution. Aims. Using observations of a dense filamentary cloud G035.39-00.33, we aim to characterise the dust emission properties and the variations of the polarisation fraction. Methods. JCMT SCUBA-2/POL-2 observations at 850 μm were combined with Planck 850 μm(353 GHz) data to map polarisation fraction at small and large scales. With previous total intensity SCUBA-2 observations (450 and 850 μm) and Herschel data, the column densities were determined via modified black-body fits and via radiative transfer modelling. Models were constructed to examine how the observed polarisation angles and fractions depend on potential magnetic field geometries and grain alignment processes. Results. POL-2 data show clear changes in the magnetic field orientation. These are not in contradiction with the uniform orientation and almost constant polarisation fraction seen by Planck, because of the difference in the beam sizes and the POL-2 data being affected by spatial filtering. The filament has a peak column density of N(H2) ~ 7 × 1022 cm−2, a minimum dust temperature of T ~ 12 K, and a mass of ~4300 M⊙ for the area N(H2) > 5 × 1021 cm−2. The estimated average value of the dust opacity spectral index is β ~ 1.9. The ratio of sub-millimetre and J-band optical depths is τ (250 μm)∕τ (J) ~ 2.5 × 10−3, more than four times the typical values for diffuse medium. The polarisation fraction decreases as a function of column density to p ~ 1% in the central filament. Because of noise, the observed decrease of p(N) is significant only at N(H2) > 2 × 1022 cm−2. The observations suggest that the grain alignment is not constant. Although the data can be explained with a complete loss of alignment at densities above ~104 cm−3 or using the predictions of radiative torques alignment, the uncertainty of the field geometry and the spatial filtering of the SCUBA-2 data prevent strong conclusions. Conclusions. The G035.39-00.33 filament shows strong signs of dust evolution and the low polarisation fraction is suggestive of a loss of polarised emission from its densest parts.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.041
Threshold uncertainty score0.081

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.209
Teacher spread0.202 · 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 source (direct Gemma or distilled Codex), 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

Citations30
Published2018
Admission routes1
Has abstractyes

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