Local SDSS galaxies in the Herschel Stripe 82 survey: a critical assessment of optically derived star formation rates
Bibliographic record
Abstract
We study a set of 3319 galaxies in the redshift interval 0.04 < z < 0.15 with far-infrared (FIR) coverage from the Herschel Stripe 82 survey (HerS), and emission-line measurements, redshifts, stellar masses and star formation rates (SFRs) from the Sloan Digital Sky Survey (SDSS) (DR7) MPA/JHU data base. About 40 per cent of the sample are detected in the Herschel/SPIRE 250 μm band. Total infrared (TIR) luminosities derived from HerS and Wide-field Infrared Survey Explorer (WISE) photometry allow us to compare infrared and optical estimates of SFR with unprecedented statistics for diverse classes of galaxies. We find excellent agreement between TIR-derived and emission line-based SFRs for H ii galaxies. Other classes, such as active galaxies and evolved galaxies, exhibit systematic discrepancies between optical and TIR SFRs. We demonstrate that these offsets are attributable primarily to survey biases and the large intrinsic uncertainties of the Dn4000- and colour-based optical calibrations used to estimate the SDSS SFRs of these galaxies. Using a classification scheme which expands upon popular emission-line methods, we demonstrate that emission-line galaxies with uncertain classifications include a population of massive, dusty, metal-rich star-forming systems that are frequently neglected in existing studies. We also study the capabilities of infrared selection of star-forming galaxies. FIR selection reveals a substantial population of galaxies dominated by cold dust which are missed by the long-wavelength WISE bands. Our results demonstrate that Herschel large-area surveys offer the means to construct large, relatively complete samples of local star-forming galaxies with accurate estimates of SFR that can be used to study the interplay between nuclear activity and star formation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".