Herschel Optimized Tau and Temperature (HOTT) Maps: Uncertainty Analysis and Robust Parameter Extraction
Bibliographic record
Abstract
Abstract We introduce the HOTT dust optical depth and temperature maps parameterizing thermal dust emission. Such maps have revolutionized studies of the distribution of matter in molecular clouds and processes relevant to star formation, including virial stability. HOTT maps for a suite of fields, including the Herschel Gould Belt Survey, are available online. The standardization of our robust pipeline for modified blackbody fitting of the spectral energy distribution (SED) of high-quality archival submillimeter data from the Herschel Space Observatory is based on a thorough analysis and quantification of the uncertainties of the data. This enables proper weighting in the SED fits. The uncertainties assessed fall into four main categories: instrument noise; the cosmic infrared background anisotropy, a contaminating sky signal; gradient-related noise arising because of dust signal morphology; and calibration uncertainty, scaling with the signal strength. Zero-level adjustments are important too. An analysis of residuals from the SED fits across many fields supports the overall appropriateness of the assumed modified blackbody model and points to where it breaks down. Finding χ 2 distributions close to the theoretical expectation boosts confidence in the pipeline and the optimized quality of the parameter maps and their estimated uncertainties. We compared our HOTT parameter maps to those from earlier studies to understand and quantify the potential for systematic differences.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".