Dissecting the origin of the submillimetre emission in nearby galaxies with Herschel and LABOCA
Bibliographic record
Abstract
We model the infrared to submillimetre spectral energy distribution of 11 nearby galaxies of the Key Insights on Nearby Galaxies: A Far-Infrared Survey with Herschel sample using Spitzer and Herschel data and compare model extrapolations at 870 μm (using different fitting techniques) with Large APEX BOlometer CAmera (LABOCA) 870 μm observations. We investigate how the differences between predictions and observations vary with model assumptions or environment. At global scales, we find that modified blackbody models using realistic cold emissivity indices (βc = 2 or 1.5) are able to reproduce the 870 μm observed emission within the uncertainties for most of the sample. Low values (βc < 1.3) would be required in NGC 0337, NGC 1512 and NGC 7793. At local scales, we observe a systematic 870 μm excess when using βc = 2.0. The βc = 1.5 or the Draine & Li (2007) models can reconcile predictions with observations in part of the discs. Some of the remaining ‘excesses’ occur towards the centres and can be partly or fully accounted for by non-dust contributions such as CO(3–2) or, to a lesser extent, free–free or synchrotron emission. In three non-barred galaxies, the remaining excesses rather occur in the disc outskirts. This could be a sign of a flattening of the submm slope (and decrease of the effective emissivity index) with radius in these objects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".