The LOFAR Two-metre Sky Survey Deep fields
Bibliographic record
Abstract
The far-infrared radio correlation (FIRC) is one of the strongest correlations in astronomy, yet a model that explains this comprehensively does not exist. The new LOFAR all Sky Survey (LoTSS) deep field, ELAIS-N1, allows exploration of this relation in previously unexplored regions of parameter space of radio frequency (150 MHz), luminosity (L150 < 1024.7), redshift (z ∼ 1), and stellar mass M* < 1011.4. We present accurate deblended far-infrared (FIR) flux measurements with robust errors at 24, 100, 160, 250, 350, and 500 μm from Spitzer and the Herschel Space Observatory using XID+. We find that the FIRC has a strong mass dependence, the evolution of which takes the form qTIR(M*) = (2.00 ± 0.01)+(−0.22 ± 0.02)(log(M/M*)−10.05). This matches recent findings in regards to the star formation rate–radio luminosity relation at 150 MHz and results from radio observations in COSMOS at 1.4 GHz with the Jansky Very Large Array (JVLA). Our results provide tighter constraints on the low-redshift end of the FIRC and at lower frequency than the COSMOS observations. In addition, we find a mild evolution with redshift, with a best fit relation qTIR(z) = (1.94 ± 0.01)(1 + z)−0.04 ± 0.01. This evolution is shallower than that suggested by previous results at 150 MHz with the differences explained by the fact that previous studies did not account for the mass dependence. Finally, we present deblended FIR fluxes for 79 609 galaxies across the LoTSS deep fields: Boötes, ELAIS-N1, and Lockman Hole.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".