Quantitative morphology of galactic cirrus in deep optical imaging
Bibliographic record
Abstract
Imaging of optical Galactic cirrus, the spatially resolved form of diffuse Galactic light, provides important insights into the properties of the diffuse interstellar medium (ISM) in the Milky Way. While previous investigations have focused mainly on the intensity characteristics of optical cirrus, their morphological properties remain largely unexplored. In this study, we employ several complementary statistical approaches – local intensity statistics, angular power spectrum and Δ-variance analysis, and wavelet scattering transform analysis – to characterize the morphology of cirrus in deep optical imaging data. We place our investigation of optical cirrus into a multiwavelength context by comparing the morphology of cirrus seen with the Dragonfly Telephoto Array to that seen with space-based facilities working at longer wavelengths (Herschel 250 μm, WISE 12 μm, and Planck radiance), as well as with structures seen in the DHIGLS HI column density map. Our statistical methods quantify the similarities and the differences of cirrus morphology in all these datasets. The morphology of cirrus at visible wavelengths resembles that of far-infrared cirrus more closely than that of mid-infrared cirrus; on small scales, anisotropies in the cosmic infrared background and systematics may lead to differences. Across all dust tracers, cirrus morphology can be well described by a power spectrum with a common power-law index γ ~ −2.9. We demonstrate quantitatively that optical cirrus exhibits filamentary, coherent structures across a broad range of angular scales. Our results offer promising avenues for linking the analysis of coherent structures in optical cirrus to the underlying physical processes in the ISM that shape them. Furthermore, we demonstrate that these morphological signatures can be leveraged to distinguish and disentangle cirrus from extragalactic light.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".