The merger rates and sizes of galaxies across the peak epoch of star formation from the HiZELS survey
Bibliographic record
Abstract
We use the HiZELS narrow-band Hα survey in combination with CANDELS, UKIDSS and WIRDS near-infrared imaging, to investigate the morphologies, merger rates and sizes of a sample of Hα emitting galaxies in the redshift range z = 0.40–2.23, an epoch encompassing the rise to the peak of the star formation rate density. Merger rates are estimated from space- and ground-based imaging using the M20 coefficient. To account for the increase in the specific star formation rate (sSFR) of the star forming ‘main sequence’ with redshift, we normalize the star formation rates of galaxies at each epoch to the typical value derived from the Hα luminosity function. Once this trend in sSFR is removed we see no evidence for an increase in the number density of star-forming galaxies or the merger rate with redshift. We thus conclude that neither is the main driver of the enhanced star-formation rate density at z ∼ 1–2, with secular processes such as instabilities within efficiently fuelled, gas-rich discs or multiple minor mergers the most likely alternatives. However, we find that ∼40–50 per cent of starburst galaxies, those with enhanced specific star formation at their epoch, are major mergers and this fraction is redshift independent. Finally, we find the surprising result that the typical size of a star-forming galaxy of a given mass does not evolve across the redshift range considered, suggesting a universal size–mass relation. Taken in combination, these results indicate a star-forming galaxy population that is statistically similar in physical size, merger rate and mass over the ∼6 Gyr covered in this study, despite the increase in typical sSFR.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".