MIGHTEE-H <scp>i</scp> : the direct detection of neutral hydrogen in galaxies at <i>z</i> &gt; 0.25
Bibliographic record
Abstract
ABSTRACT Atomic hydrogen constitutes the gas reservoir from which molecular gas and star formation in galaxies emerges. However, the weakness of the line means it has been difficult to directly detect in all but the very local Universe. Here, we present results from the first search using the MeerKAT International Tiered Extragalactic Exploration (MIGHTEE) Survey for high-redshift ($z>0.25$) H i emission from individual galaxies. By searching for 21-cm emission centred on the position and redshift of optically selected emission-line galaxies we overcome difficulties that hinder untargeted searches. We detect 11 galaxies at $z>0.25$, forming the first sample of $z>0.25$ detections with an interferometer, with the highest redshift detection at $z = 0.3841$. We find they have much larger H i masses than their low-redshift H i-selected counterparts for a given stellar mass. This can be explained by the much larger cosmological volume probed at these high redshifts, and does not require any evolution of the H i mass function. We make the first-ever measurement of the baryonic Tully–Fisher relation (bTFr) with H i at $z>0.25$ and find consistency with the local bTFr, but with tentative evidence of a flattening in the relation at these redshifts for higher-mass objects. This may signify evolution, in line with predictions from hydrodynamic simulations, or that the molecular gas mass in these high-mass galaxies could be significant. This study paves the way for future studies of H i beyond the local Universe, using both searches targeted at known objects and via pure H i selection.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".