Aperture-free star formation rate of SDSS star-forming galaxies
Bibliographic record
Abstract
Large area surveys with a high number of galaxies observed have undoubtedly marked a milestone in the understanding of several properties of galaxies, such as star-formation history, morphology, and metallicity. However, in many cases, these surveys provide fluxes from fixed small apertures (e.g. fibre), which cover a scant fraction of the galaxy, compelling us to use aperture corrections to study the global properties of galaxies. In this work, we derive the current total star formation rate (SFR) of Sloan Digital Sky Survey (SDSS) star-forming galaxies, using an empirically based aperture correction of the measured Hα flux for the first time, thus minimising the uncertainties associated with reduced apertures. All the Hα fluxes have been extinction-corrected using the Hα/ Hβ ratio free from aperture effects. The total SFR for ~210 000 SDSS star-forming galaxies has been derived applying pure empirical Hα and Hα/ Hβ aperture corrections based on the Calar Alto Legacy Integral Field Area (CALIFA) survey. We find that, on average, the aperture-corrected SFR is ~0.65 dex higher than the SDSS fibre-based SFR. The relation between the SFR and stellar mass for SDSS star-forming galaxies (SFR-M⋆) has been obtained, together with its dependence on extinction and Hα equivalent width. We compare our results with those obtained in previous works and examine the behaviour of the derived SFR in six redshift bins, over the redshift range 0.005 ≤ z ≤ 0.22. The SFR-M⋆ sequence derived here is in agreement with selected observational studies based on integral field spectroscopy of individual galaxies as well as with the predictions of recent theoretical models of disc galaxies.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".