DELAY SPECTRUM WITH PHASE-TRACKING ARRAYS: EXTRACTING THE H i POWER SPECTRUM FROM THE EPOCH OF REIONIZATION
Bibliographic record
Abstract
ABSTRACT The detection of redshifted 21 cm emission from the epoch of reionization (EoR) is a challenging task owing to strong foregrounds that dominate the signal. In this paper, we propose a general method, based on the delay spectrum approach, to extract H i power spectra that are applicable to tracking observations using an imaging radio interferometer ("Delay Spectrum with Imaging Arrays"). Our method is based on modeling the H i signal taking into account the impact of wide field effects such as the w-term, which are then used as appropriate weights in cross-correlating the measured visibilities. Our method is applicable to any radio interferometer that tracks a phase center and could be utilized for arrays such as the Murchison Widefield Array (MWA), Low Frequency Array (LOFAR), Giant Meterwave Radio Telescope (GMRT), Donald C. Backer Precision Array for Probing the Epoch of Reionization (PAPER), and HERA. In the literature the delay spectrum approach has been implemented for near-redundant baselines using drift scan observations. In this paper we explore the scheme for non-redundant tracking arrays. This is the first application of delay spectrum methodology to such data to extract the H i signal. We analyze 3 hr of MWA tracking data on the EoR1 field. We present both two-dimensional ( ) and one-dimensional (k) power spectra from the analysis. Our results are in agreement with the findings of other pipelines developed to analyze the MWA EoR data.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".