Star formation history of ≤ <i>z</i> ≤ mass-selected galaxies in the ELAIS-N1 Field
Bibliographic record
Abstract
ABSTRACT We measure the specific star formation rates (sSFRs) of K-band selected galaxies from the European Large Area ISO Survey North 1 by stacking Giant Metrewave Radio Telescope data at 610 MHz. We identify a sample of star-forming galaxies (SFGs), spanning ${0.1\le \, {z}\, \le \, 1.5}$ and $\rm {10^{8.5}\lt \, {{\mathit{ M}}_{\star }}/{{M}_{\odot }}\lt 10^{12.4}}$, using a combination of multiwavelength diagnostics obtained from the deep LOw Frequency ARray Two-metre Sky Survey multiwavelength catalogue. We measure the flux densities in the radio map and estimate the radio SFR in order to probe the nature of the galaxies below the noise and confusion limits. The massive galaxies in our sample have the lowest sSFRs, which is in agreement with previous studies. For the different populations, we show that the sSFR–mass relation steepens with redshift, with an average slope of $\rm {\langle \beta _{All} \rangle \, =\, -0.49\pm 0.01}$ for the whole sample, and $\rm {\langle \beta _{SFG} \rangle \, =\, -0.42\pm 0.02}$ for the SFGs. Our results indicate that galaxy populations undergo ’downsizing’, whereby most massive galaxies form their stars earlier and more rapidly than low-mass galaxies. Both populations show a strong decrease in their sSFR towards the present epoch. The sSFR evolution with redshift is best described by a power law ${(1\, +\, {z})^{n}}$, where $\rm {\langle {\mathit{ n}}_{ALL}\rangle \sim 4.94\pm 0.53}$ for all galaxies and $\rm {\langle {\mathit{ n}}_{SFG}\rangle \sim 3.51\pm 0.52}$ for SFGs. Comparing our measured sSFRs to results from literature, we find a general agreement in the sSFR–M⋆ plane.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".