Multiscale analysis of Galactic dust emission using complex wavelet transforms – I. Separation of Gaussian and non-Gaussian fluctuations in Herschel observations
Bibliographic record
Abstract
We use anisotropic complex wavelet transforms to make a multiscale analysis of the distribution of fluctuations of dust emission at 250 μm from the Herschel infrared Galactic Plane Survey of the Herschel Space Observatory. By reproducing the Fourier power spectrum with complex wavelet transforms we show that different power distributions at each scale can have a significant effect on the measured power law. Moreover, with an iterative algorithm we separate the Gaussian and non-Gaussian part of wavelet coefficient distributions in each scale and azimuthal direction. The reconstructed map of non-Gaussian fluctuations is well correlated to small-scale structures of the 13CO emission map as well as its power spectrum which has a power-law index of −2.3. The Gaussian part of the map shows more diffuse structures with a steeper power law of −3.1. Non-Gaussian coefficients are almost undetectable at scales greater than ∼0.15 arcmin−1 (∼4–26 pc). This characteristic scale could be associated with the size of the larger molecular cloud in the region. We also demonstrate that exponentiated Gaussian random field cannot reproduce the flatter power law of the non-Gaussian component observed in the thermal dust emission, which seems to be a property of interstellar clouds.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".