HDMClouds: a hierarchical decomposition of molecular clouds based on Gaussian mixtures
Bibliographic record
Abstract
The identification and characterization of independent entities within molecular clouds is a key challenge for astronomical data analysis. The ever-increasing volume, resolution and sensitivity of observations demand automatic routines to identify and deblend candidate entities to be analysed. Additionally, the intrinsically hierarchical nature of molecular gas distributions demands an automatic identification of the nesting relations between these entities. We propose a novel approach for decomposing molecular clouds in two steps: first we fit the data to a Gaussian mixture with many components, then reconstruct the cloud using a hierarchical model using a Gaussian-mixture reduction algorithm. We use a continuous-space representation, because it is well suited for disentangling coupled entities of emission compared with pixel-based ones, and build a tree structure to represent the hierarchical connections between mixture components. This allows us to select different groups of components in the tree without additional computational effort, including overlapping substructures. We assess our proposal quantitatively and qualitatively using data from the Atacama Large Millimeter Array (ALMA) science verification archive, as well as synthetic data. We also compare the results from some state-of-the-art clump identification algorithms. The experiments and comparisons show that our approach is an effective way to inspect and represent the hierarchical structure of molecular 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".