Towards 21-cm intensity mapping at <i>z</i> = 2.28 with uGMRT using the tapered gridded estimator – IV. Wide-band analysis
Bibliographic record
Abstract
ABSTRACT We present a Wide-band tapered gridded estimator (TGE), which incorporates baseline migration and variation of the primary beam pattern for neutral hydrogen ($H\, {\small I}$) 21-cm intensity mapping (IM) with large frequency bandwidth radio-interferometric observations. Here we have analysed $394-494 \, {\rm MHz}$ (z = 1.9–2.6) uGMRT data to estimate the Multifrequency Angular Power Spectrum (MAPS) Cℓ(Δν) from which we have removed the foregrounds using the polynomial fitting (PF) and Gaussian Process Regression (GPR) methods developed in our earlier work. Using the residual Cℓ(Δν) to estimate the mean-squared 21-cm brightness temperature fluctuation Δ2(k), we find that this is consistent with 0 ± 2σ in several k bins. The resulting 2σ upper limit $\Delta ^2(k) \lt (4.68)^2 \, \rm {mK^2}$ at $k=0.219\, \rm {Mpc^{-1}}$ is nearly 15 times tighter than earlier limits obtained from a smaller bandwidth ($24.4 \, {\rm MHz}$) of the same data. The 2σ upper limit $[\Omega _{H\, {\small I}} b_{H\, {\small I}}] \lt 1.01 \times 10^{-2}$ is within an order of magnitude of the value expected from independent estimates of the $H\, {\small I}$ mass density $\Omega _{H\, {\small I}}$ and the $H\, {\small I}$ bias $b_{H\, {\small I}}$. The techniques used here can be applied to other telescopes and frequencies, including $\sim 150 \, {\rm MHz}$ Epoch of Reionization observations.
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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 | 0.001 |
| 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) | 0.001 | 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".