A LOFAR-IRAS cross-match study: the far-infrared radio correlation and the 150 MHz luminosity as a star-formation rate tracer
Bibliographic record
Abstract
Aims. We aim to study the far-infrared radio correlation (FIRC) at 150 MHz in the local Universe (at a median redshift ⟨ z ⟩∼0.05) and improve the use of the rest-frame 150 MHz luminosity, L 150 , as a star-formation rate (SFR) tracer, which is unaffected by dust extinction. Methods. We cross-match the 60 μ m selected Revised IRAS Faint Source Survey Redshift (RIFSCz) catalogue and the 150 MHz selected LOFAR value-added source catalogue in the Hobby-Eberly Telescope Dark Energy Experiment (HETDEX) Spring Field. We estimate L 150 for the cross-matched sources and compare it with the total infrared (IR) luminosity, L IR , and various SFR tracers. Results. We find a tight linear correlation between log L 150 and log L IR for star-forming galaxies, with a slope of 1.37. The median q IR value (defined as the logarithm of the L IR to L 150 ratio) and its rms scatter of our main sample are 2.14 and 0.34, respectively. We also find that log L 150 correlates tightly with the logarithm of SFR derived from three different tracers, i.e., SFR Hα based on the H α line luminosity, SFR 60 based on the rest-frame 60 μ m luminosity and SFR IR based on L IR , with a scatter of 0.3 dex. Our best-fit relations between L 150 and these SFR tracers are, log L 150 ( L ⊙ ) = 1.35(±0.06) × log SFR H α ( M ⊙ yr −1 ) + 3.20(±0.06), log L 150 ( L ⊙ ) = 1.31(±0.05) × log SFR 60 ( M ⊙ yr −1 ) + 3.14(±0.06), and log L 150 ( L ⊙ ) = 1.37 (±0.05) × log SFR IR ( M ⊙ yr −1 ) + 3.09(±0.05), which show excellent agreement with each other.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".