The clustering of Hα emitters at<i>z</i>=2.23 from HiZELS
Bibliographic record
Abstract
We present a clustering analysis of 370 high-confidence Hα emitters (HAEs) at z = 2.23. The HAEs are detected in the Hi-Z Emission Line Survey (HiZELS), a large-area blank field 2.121 μm narrow-band survey using the United Kingdom Infrared Telescope Wide Field Camera (WFCAM). Averaging the two-point correlation function of HAEs in two ∼1° scale fields [United Kingdom Infrared Deep Sky Survey/Ultra Deep Survey (UDS) and Cosmological Evolution Survey (COSMOS) fields] we find a clustering amplitude equivalent to a correlation length of r0 = 3.7 ± 0.3 h−1 Mpc for galaxies with star formation rates of ≳7 M⊙ yr−1. The data are also well-fitted by the expected correlation function of cold dark matter (CDM), scaled by a bias factor: ωHAE = b2ωDM where . The corresponding ‘characteristic’ mass for the haloes hosting HAEs is log (Mh/[h−1 M⊙]) = 11.7 ± 0.1. Comparing to the latest semi-analytic galform predictions for the evolution of HAEs in a ΛCDM cosmology, we find broad agreement with the observations, with galform predicting an HAE correlation length of ∼4 h−1 Mpc. Motivated by this agreement, we exploit the simulations to construct a parametric model of the halo occupation distribution (HOD) of HAEs, and use this to fit the observed clustering. Our best-fitting HOD can adequately reproduce the observed angular clustering of HAEs, yielding an effective halo mass and bias in agreement with that derived from the scaled ωDM fit, but with the relatively small sample size the current data provide a poor constraint on the HOD. However, we argue that this approach provides interesting hints into the nature of the relationship between star-forming galaxies and the matter field, including insights into the efficiency of star formation in massive haloes. Our results support the broad picture that ‘typical’ (≲L⋆) star-forming galaxies have been hosted by dark matter haloes with Mh ≲ 1012 h−1 M⊙ since z ≈ 2, but with a broad occupation distribution and clustering that is likely to be a strong function of luminosity.
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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 | 0.001 | 0.001 |
| 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".