Calibration of hybrid resolved star formation rate recipes based on PHANGS–MUSE H
Bibliographic record
Abstract
Mapping star-formation rates (SFR) within galaxies is key to unveiling their assembly and evolution. Calibrations exist for computing the SFR from a combination of ultraviolet and infrared bands for galaxies as integrated systems, but their applicability to sub-galactic (kiloparsec) scales remains largely untested. We used integral field spectroscopy of 19 nearby (D < 20 Mpc) galaxies obtained by PHANGS–MUSE to derive accurate Balmer decrements (Hα/Hβ) and attenuation-corrected Hα maps. We combined this information with mid-infrared maps from WISE at 22 μm and ultraviolet maps from GALEX in the far-UV band to derive SFR surface densities in nearby galaxies on resolved (kiloparsec) scales. Using the Hα attenuation-corrected SFR as a reference, we find that hybrid recipes from the literature overestimate the SFR in regions of low SFR surface density, low specific star-formation rate (sSFR), low attenuation, and old stellar ages. We attribute these trends to heating of the dust by old stellar populations (IR cirrus). We calibrated this effect by proposing functional forms for the coefficients in front of the IR term that depend on band ratios sensitive to the sSFR. These recipes return SFR estimates that agree with those in the literature at high sSFR (log(sSFR/yr−1) > − 9.9). Moreover, they lead to negligible bias and < 0.16 dex scatter when compared to our reference attenuation-corrected SFR from Hα. These calibrations prove reliable as a function of physical scale. In particular, they agree within 10% with the attenuation corrections computed from the Balmer decrement on 100 pc scales. Despite small quantitative differences, our calibrations are also applicable to integrated galaxy scales probed by the MaNGA survey, but with a larger scatter (up to 0.22 dex). Observations with JWST open up the possibility to calibrate these relations in nearby galaxies with cloud-scale (∼100 pc) resolution mid-IR imaging.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".