The JCMT nearby galaxies legacy survey: SCUBA-2 observations of nearby galaxies
Bibliographic record
Abstract
ABSTRACT We present 850 $\mu$m observations of a sample of 8 nearby spiral galaxies, made using the SCUBA-2 camera on the James Clerk Maxwell Telescope (JCMT) as part of the JCMT Nearby Galaxies Legacy Survey (NGLS). We corrected our data for the presence of the 12CO J = 3 → 2 line in the SCUBA-2 850 $\mu$m bandwidth using NGLS HARP data, finding a typical 12CO contribution of ∼20 per cent. We measured dust column densities, temperatures, and opacity indices by fitting spectral energy distributions constructed from SCUBA-2 and archival Herschel observations, and used archival GALEX and Spitzer data to make maps of surface density of star formation ($\Sigma _{\small {\rm SFR}}$). Typically, comparing SCUBA-2-derived H2 surface densities ($\Sigma _{\rm H_2}$) to $\Sigma _{\small {\rm SFR}}$ gives shallow star formation law indices within galaxies, with SCUBA-2-derived values typically being sublinear and Herschel-derived values typically being broadly linear. This difference is likely due to the effects of atmospheric filtering on the SCUBA-2 data. Comparing the mean values of $\Sigma _{\rm H_2}$ and $\Sigma _{\small {\rm SFR}}$ of the galaxies in our sample returns a steeper star formation law index, broadly consistent with both the Kennicutt–Schmidt value of 1.4 and linearity. Our results show that a SCUBA-2 detection is a good predictor of star formation. We suggest that Herschel emission traces gas in regions which will form stars on time-scales ∼5 − 100 Myr, comparable to the star formation time-scale traced by GALEX and Spitzer data, while SCUBA-2 preferentially traces the densest gas within these regions, which likely forms stars on shorter time-scales.
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 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".