BLAST: the far-infrared/radio correlation in distant galaxies
Bibliographic record
Abstract
We investigate the correlation between far-infrared (FIR) and radio luminosities in distant galaxies, a lynchpin of modern astronomy. We use data from the Balloon-borne Large Aperture Submillimetre Telescope (BLAST), Spitzer, the Large Apex BOlometer CamerA (LABOCA), the Very Large Array and the Giant Metre-wave Radio Telescope (GMRT) in the Extended Chandra Deep Field South (ECDFS). For a catalogue of BLAST 250-μm-selected galaxies, we remeasure the 70–870-μm flux densities at the positions of their most likely 24-μm counterparts, which have a median [interquartile] redshift of 0.74 [0.25, 1.57]. From these, we determine the monochromatic flux density ratio, q250(= log10[S250 μm/S1400 MHz]), and the bolometric equivalent, qIR. At z≈ 0.6, where our 250-μm filter probes rest-frame 160-μm emission, we find no evolution relative to q160 for local galaxies. We also stack the FIR and submm images at the positions of 24-μm- and radio-selected galaxies. The difference between qIR seen for 250-μm- and radio-selected galaxies suggests that star formation provides most of the IR luminosity in ≲100-μJy radio galaxies, but rather less for those in the mJy regime. For the 24-μm sample, the radio spectral index is constant across 0 < z < 3, but qIR exhibits tentative evidence of a steady decline such that qIR∝ (1 +z)−0.15±0.03– significant evolution, spanning the epoch of galaxy formation, with major implications for techniques that rely on the FIR/radio correlation. We compare with model predictions and speculate that we may be seeing the increase in radio activity that gives rise to the radio background.
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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 | 0.002 |
| 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".