Deep uGMRT observations of the ELAIS-North 1 field: statistical properties of radio–infrared relations up to <i>z</i> ∼ 2
Bibliographic record
Abstract
ABSTRACT Comprehending the radio–infrared (IR) relations of the faint extragalactic radio sources is important for using radio emission as a tracer of star formation in high redshift (z) star-forming galaxies (SFGs). Using deep uGMRT observations of the ELAIS-N1 field in the 0.3–0.5 GHz range, we study the statistical properties of the radio–IR relations and the variation of the ‘q-parameter’ up to z = 2 after broadly classifying the faint sources as SFGs and AGN. We find the dust temperature (Tdust) to increase with z. This gives rise to $q_{\rm 24\,\mu m}$, measured at $24\, \mu$m, to increase with z as the peak of IR emission shifts towards shorter wavelengths, resulting in the largest scatter among different measures of q-parameters. $q_{\rm 70\,\mu m}$ measured at $70\, \mu$m, and qTIR using total-IR (TIR) emission are largely unaffected by Tdust. We observe strong, non-linear correlations between the radio luminosities at 0.4 and 1.4 GHz with $70\, \mu$m luminosity and TIR luminosity(LTIR). To assess the possible role of the radio-continuum spectrum in making the relations non-linear, for the first time we study them at high z using integrated radio luminosity (LRC) in the range 0.1–2 GHz. In SFGs, the LRC–LTIR relation remains non-linear with a slope of 1.07 ± 0.02, has a factor of 2 lower scatter compared to monochromatic radio luminosities, and $q^{\rm RC}_{\rm TIR}$ decreases with z as $q^{\rm RC}_{\rm TIR}= (2.27 \pm 0.03)\, (1+z)^{-0.12 \pm 0.03}$. A redshift variation of q is a natural consequence of non-linearity. We suggest that a redshift evolution of magnetic field strengths and/or cosmic ray acceleration efficiency in high-z SFGs could give rise to non-linear radio–IR relations.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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 | 0.000 |
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".