The far-infrared/radio correlation as probed by<i>Herschel</i>
Bibliographic record
Abstract
We set out to determine the ratio, qIR, of rest-frame 8–1000-μm flux, SIR, to monochromatic radio flux, S1.4 GHz, for galaxies selected at far-infrared (IR) and radio wavelengths, to search for signs that the ratio evolves with redshift, luminosity or dust temperature, Td, and to identify any far-IR-bright outliers – useful laboratories for exploring why the far-IR/radio correlation (FIRRC) is generally so tight when the prevailing theory suggests variations are almost inevitable. We use flux-limited 250-μm and 1.4-GHz samples, obtained using Herschel and the Very Large Array (VLA) in GOODS-North (-N). We determine bolometric IR output using ten bands spanning λobs = 24–1250 μm, exploiting data from PACS and SPIRE (PEP; HerMES), as well as Spitzer, SCUBA, AzTEC and MAMBO. We also explore the properties of an LIR-matched sample, designed to reveal evolution of qIR with redshift, spanning log LIR = 11–12 and z = 0–2, by stacking into the radio and far-IR images. For 1.4-GHz-selected galaxies in GOODS-N, we see tentative evidence of a break in the flux ratio, qIR, at L1.4 GHz ~ 1022.7 W Hz-1, where active galactic nuclei (AGN) are starting to dominate the radio power density, and of weaker correlations with redshift and Td. From our 250-μm-selected sample we identify a small number of far-IR-bright outliers, and see trends of qIR with L1.4 GHz, LIR, Td and redshift, noting that some of these are inter-related. For our LIR-matched sample, there is no evidence that qIR changes significantly as we move back into the epoch of galaxy formation: we find qIR (1+z)γ, where γ = -0.04±0.03 at z = 0–2; however, discounting the least reliable data at z < 0.5 we find γ = -0.26±0.07, modest evolution which may be related to the radio background seen by ARCADE 2, perhaps driven by <10-μJy radio activity amongst ordinary star-forming galaxies at z>1.
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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 | 0.001 | 0.002 |
| 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) | 0.003 | 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".