H <scp>i</scp>intensity mapping with MeerKAT: calibration pipeline for multidish autocorrelation observations
Bibliographic record
Abstract
ABSTRACT While most purpose-built 21-cm intensity mapping experiments are close-packed interferometer arrays, general-purpose dish arrays should also be capable of measuring the cosmological 21-cm signal. This can be achieved most efficiently if the array is used as a collection of scanning autocorrelation dishes rather than as an interferometer. As a first step towards demonstrating the feasibility of this observing strategy, we show that we are able to successfully calibrate dual-polarization autocorrelation data from 64 MeerKAT dishes in the L band (856–1712 MHz, 4096 channels), with 10.5 h of data retained from six nights of observing. We describe our calibration pipeline, which is based on multilevel radio frequency interference flagging, periodic noise diode injection to stabilize gain drifts, and an absolute calibration based on a multicomponent sky model. We show that it is sufficiently accurate to recover maps of diffuse celestial emission and point sources over a 10° × 30° patch of the sky overlapping with the WiggleZ 11-h field. The reconstructed maps have a good level of consistency between per-dish maps and external data sets, with the estimated thermal noise limited to 1.4 × the theoretical noise level (∼2 mK). The residual maps have rms amplitudes below 0.1 K, corresponding to $\lt 1{{\ \rm per\ cent}}$ of the model temperature. The reconstructed Galactic H i intensity map shows excellent agreement with the Effelsberg–Bonn H i Survey, and the flux of the radio galaxy 4C + 03.18 is recovered to within 3.6 per cent, which demonstrates that the autocorrelation can be successfully calibrated to give the zero-spacing flux and potentially help in the imaging of MeerKAT interferometric data. Our results provide a positive indication towards the feasibility of using MeerKAT and the future Square Kilometre Array to measure the H i intensity mapping signal and probe cosmology on degree scales and above.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.010 |
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".