A Study of 90 GHz Dust Emissivity on Molecular Cloud and Filament Scales
Bibliographic record
Abstract
Abstract Recent observations from the MUSTANG2 instrument on the Green Bank Telescope have revealed evidence of enhanced long-wavelength emission in the dust spectral energy distribution (SED) in the Orion Molecular Cloud (OMC) 2/3 filament on 25″ (0.1 pc) scales. Here we present a measurement of the SED on larger spatial scales (map size 0.°5–3° or 3–20 pc), at somewhat lower resolution (120″, corresponding to 0.25 pc at 400 pc) using data from the Herschel satellite and Atacama Cosmology Telescope (ACT). We then extend the 120″-scale investigation to other regions covered in the Herschel Gould Belt Survey (HGBS), specifically the dense filaments in the southerly regions of Orion A, Orion B, and Serpens-S. Our data set in aggregate covers approximately 10 deg2, with continuum photometry spanning from 160 μm to 3 mm. These OMC 2/3 data display excess emission at 3 mm, though less (10.9% excess) than what is seen at higher resolution. Strikingly, we find that the enhancement is present even more strongly in the other filaments we targeted, with an average excess of 42.4% and 30/46 slices showing an inconsistency with the modified blackbody to at least 4σ. Applying this analysis to the other targeted regions, we lay the groundwork for future high-resolution analyses. Additionally, we also consider a two-component dust model motivated by Planck results and an amorphous grain dust model. While both of these have been proposed to explain deviations in emission from a generic modified blackbody, we find that they have significant drawbacks, requiring many spectral points or lacking experimental data coverage.
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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 | 0.000 | 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".