The e-MERGE Survey (e-MERLIN Galaxy Evolution Survey): overview and survey description
Bibliographic record
Abstract
ABSTRACT We present an overview and description of the e-MERGE Survey (e-MERLIN Galaxy Evolution Survey) Data Release 1 (DR1), a large program of high-resolution 1.5-GHz radio observations of the GOODS-N field comprising ∼140 h of observations with enhanced-Multi-Element Remotely Linked Interferometer Network (e-MERLIN) and ∼40 h with the Very Large Array (VLA). We combine the long baselines of e-MERLIN (providing high angular resolution) with the relatively closely packed antennas of the VLA (providing excellent surface brightness sensitivity) to produce a deep 1.5-GHz radio survey with the sensitivity (${\sim}1.5\, \mu$ Jy beam−1), angular resolution (0.2–0.7 arcsec) and field-of-view (∼15 × 15 arcmin2) to detect and spatially resolve star-forming galaxies and active galactic nucleus (AGN) at $z$ ≳ 1. The goal of e-MERGE is to provide new constraints on the deep, sub-arcsecond radio sky which will be surveyed by SKA1-mid. In this initial publication, we discuss our data analysis techniques, including steps taken to model in-beam source variability over an ∼20-yr baseline and the development of new point spread function/primary beam models to seamlessly merge e-MERLIN and VLA data in the uv plane. We present early science results, including measurements of the luminosities and/or linear sizes of ∼500 galaxies selected at 1.5 GHz. In combination with deep Hubble Space Telescope observations, we measure a mean radio-to-optical size ratio of re-MERGE/rHST ∼ 1.02 ± 0.03, suggesting that in most high-redshift galaxies, the ∼GHz continuum emission traces the stellar light seen in optical imaging. This is the first in a series of papers that will explore the ∼kpc-scale radio properties of star-forming galaxies and AGN in the GOODS-N field observed by e-MERGE DR1.
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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 | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".