MHONGOOSE: A MeerKAT nearby galaxy H I survey
Bibliographic record
Abstract
The MHONGOOSE (MeerKAT H I Observations of Nearby Galactic Objects: Observing Southern Emitters) survey maps the distribution and kinematics of the neutral atomic hydrogen (H I ) gas in and around 30 nearby star-forming spiral and dwarf galaxies to extremely low H I column densities. The H I column density sensitivity (3 σ over 16 km s −1 ) ranges from ∼5 × 10 17 cm −2 at 90″ resolution to ∼4 × 10 19 cm −2 at the highest resolution of 7″. The H I mass sensitivity (3 σ over 50 km s −1 ) is ∼5.5 × 10 5 M ⊙ at a distance of 10 Mpc (the median distance of the sample galaxies). The velocity resolution of the data is 1.4 km s −1 . One of the main science goals of the survey is the detection of cold accreting gas in the outskirts of the sample galaxies. The sample was selected to cover a range in H I masses from 10 7 M ⊙ to almost 10 11 M ⊙ in order to optimally sample possible accretion scenarios and environments. The distance to the sample galaxies ranges from 3 to 23 Mpc. In this paper, we present the sample selection, survey design, and observation and reduction procedures. We compared the integrated H I fluxes based on the MeerKAT data with those derived from single-dish measurement and find good agreement, indicating that our MeerKAT observations are recovering all flux. We present H I moment maps of the entire sample based on the first ten percent of the survey data, and find that a comparison of the zeroth- and second-moment values shows a clear separation in the physical properties of the H I between areas with star formation and areas without related to the formation of a cold neutral medium. Finally, we give an overview of the H I -detected companion and satellite galaxies in the 30 fields, five of which have not previously been cataloged. We find a clear relation between the number of companion galaxies and the mass of the main target galaxy.
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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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".