Multi-frequency angular power spectrum of the 21 cm signal from the Epoch of Reionisation using the Murchison Widefield Array
Bibliographic record
Abstract
Context. The Multi-frequency Angular Power Spectrum (MAPS) is an alternative to spherically averaged power spectra, and computes local fluctuations in the angular power spectrum without need for line-of-sight spectral transform. Aims. We aimed to test different approaches to MAPS and treatment of the foreground contamination, and compare with the spherically averaged power spectrum, and the single-frequency angular power spectrum. Methods. We applied the MAPS to 110 h of data in z = 6.2 − 7.5 obtained for the Murchison Widefield Array Epoch of Reionisation experiment to compute the statistical power of 21 cm brightness temperature fluctuations. In the presence of bright foregrounds, a filter was applied to remove large-scale modes prior to MAPS application, significantly reducing MAPS power due to systematics. Results. The MAPS showed a contrast of 102–103 to a simulated 21 cm cosmological signal for spectral separations of 0−4 MHz after application of the filter, reflecting results for the spherically averaged power spectrum. The single-frequency angular power spectrum was also computed. At z = 7.5 and l = 200, we found an angular power of 53 mK2, exceeding a simulated cosmological signal power by a factor of one thousand. Residual spectral structure, inherent to the calibrated data, and not spectral leakage from large-scale modes, was the dominant source of systematic power bias. The single-frequency angular power spectrum yielded slightly poorer results compared with the spherically averaged power spectrum, having applied a spectral filter to reduce foregrounds. Exploration of other filters may improve this result, along with consideration of wider bandwidths.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".