CHANGES OF DUST OPACITY WITH DENSITY IN THE ORION A MOLECULAR CLOUD
Bibliographic record
Abstract
We have studied the opacity of dust grains at submillimeter wavelengths by estimating the optical depth from imaging at 160, 250, 350, and 500 μm from the Herschel Gould Belt Survey and comparing this to a column density obtained from the Two Micron All Sky Survey derived color excess E ( J − K s ). Our main goal was to investigate the spatial variations of the opacity due to "big" grains over a variety of environmental conditions and thereby quantify how emission properties of the dust change with column (and volume) density. The central and southern areas of the Orion A molecular cloud examined here, with N H ranging from 1.5 × 10 21 cm −2 to 50 × 10 21 cm −2 , are well suited to this approach. We fit the multi-frequency Herschel spectral energy distributions (SEDs) of each pixel with a modified blackbody to obtain the temperature, T , and optical depth, τ 1200 , at a fiducial frequency of 1200 GHz (250 μm). Using a calibration of N H / E ( J − K s ) for the interstellar medium (ISM) we obtained the opacity (dust emission cross-section per H nucleon), σ e (1200), for every pixel. From a value ∼1 × 10 −25 cm 2 H −1 at the lowest column densities that is typical of the high-latitude diffuse ISM, σ e (1200) increases as N 0.28 H over the range studied. This is suggestive of grain evolution. Integrating the SEDs over frequency, we also calculated the specific power P (emission power per H) for the big grains. In low column density regions where dust clouds are optically thin to the interstellar radiation field (ISRF), P is typically 3.7 × 10 −31 W H −1 , again close to that in the high-latitude diffuse ISM. However, we find evidence for a decrease of P in high column density regions, which would be a natural outcome of attenuation of the ISRF that heats the grains, and for localized increases for dust illuminated by nearby stars or embedded protostars.
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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 | 0.001 | 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".