<scp>H i</scp> intensity mapping with MeerKAT: primary beam effects on foreground cleaning
Bibliographic record
Abstract
ABSTRACT Upcoming and future neutral hydrogen Intensity Mapping surveys offer a great opportunity to constrain cosmology in the post-reionization Universe, provided a good accuracy is achieved in the separation between the strong foregrounds and the cosmological signal. Cleaning methods are often applied under the assumption of a simplistic Gaussian primary beam. In this work, we test the cleaning in the presence of a realistic primary beam model with a non-trivial frequency dependence. We focus on the Square Kilometre Array precursor MeerKAT telescope and simulate a single-dish wide-area survey. We consider the main foreground components, including an accurate full-sky point source catalogue. We find that the coupling between beam sidelobes and the foreground structure can complicate the cleaning. However, when the beam frequency dependence is smooth, we show that the cleaning is only problematic if the far sidelobes are unexpectedly large. Even in that case, a proper reconstruction is possible if the strongest point sources are removed and the cleaning is more aggressive. We then consider a non-trivial frequency dependence: a sinusoidal type feature in the beamwidth that is present in the MeerKAT beam and is expected in most dishes, including SKA1-MID. Such a feature, coupling with the foreground emission, biases the reconstruction of the signal across frequency, potentially impacting the cosmological analysis. We show that this effect is constrained to a narrow region in k∥ space and can be reduced if the maps are carefully re-smoothed to a common lower resolution.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".