<scp>Hi</scp> intensity mapping with the MIGHTEE Survey: first results of the <scp>Hi</scp> power spectrum
Bibliographic record
Abstract
ABSTRACT We present the first results of the H i intensity mapping power spectrum analysis with the MeerKAT International GigaHertz Tiered Extragalactic Exploration (MIGHTEE) survey. We use data covering $\sim$ 4 square degrees in the COSMOS field using a frequency range of 962.5–1008.42 MHz, equivalent to H i emission in $0.4< z< 0.48$. The data consist of 15 pointings with a total of 94.2 h on-source. We verify the suitability of the MIGHTEE data for H i intensity mapping by testing for residual systematics across frequency, baselines, and pointings. We also vary the window used for H i signal measurements and find no significant improvement using stringent Fourier mode cuts. We compute the H i power spectrum at scales $0.5\, \textrm {Mpc}^{-1} \lesssim k \lesssim 10\, \textrm {Mpc}^{-1}$ in autocorrelation as well as cross-correlation between observational scans using power spectrum domain averaging for pointings. We report consistent upper limits of 29.8 mK$^{2}$ Mpc${^3}$ from the 2$\sigma$ cross-correlation measurements and 25.82 mK$^{2}$ Mpc${^3}$ from autocorrelation at $k\sim$2 Mpc$^{-1}$.The low signal-to-noise ratio in this data potentially limits our ability to identify residual systematics, which will be addressed in the future by incorporating more data in the analysis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".