Joint Modelling of Dust Scattering and Thermal Emission: The Spider Complex
Bibliographic record
Abstract
Abstract Observations across the electromagnetic spectrum of radiative processes involving interstellar dust—emission, absorption, and scattering—are used to constrain the parameters of dust models and more directly to aid in foreground removal of dust for extragalactic and cosmological observations. Dust models can benefit from more independent constraints from complementary observations. Here, we quantify the relationship between scattered light and thermal emission from dust in a diffuse (cirrus) intermediate-latitude cloud, Spider, using data from the Dragonfly Telephoto Array and the Herschel Space Observatory. A challenge for optical observations of faint diffuse cirrus is accurate removal of a contaminating, spatially varying sky. We present a technique to analyze two images of the same cirrus field concurrently, correlating pixel values to capture the relationship and simultaneously fitting the sky-related signal as a complex noncorrelating additive component. For the Spider, we measure a color g − r = 0.644 ± 0.024 and ratios of visible-wavelength to 250 μm intensity of γ g,250 = (0.855 ± 0.025) × 10−3 and γ r,250 = (1.55 ± 0.08) × 10−3 for the g and r-bands, respectively. We show how to use any dust model that matches the thermal dust emission to predict an upper limit to the amount of scattered light. The actual brightness of the cirrus will be fainter than this limit because of anisotropic scattering by the dust combined with anisotropy of the incident interstellar radiation field (ISRF). Using models of dust and the ISRF in the literature, we illustrate that the predicted brightness is indeed lower, though not as faint as the observations indicate.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".