Towards 21-cm intensity mapping at <i>z</i> = 2.28 with uGMRT using the tapered gridded estimator – II. Cross-polarization power spectrum
Bibliographic record
Abstract
ABSTRACT Neutral hydrogen (H i) 21-cm intensity mapping (IM) offers an efficient technique for mapping the large-scale structures in the Universe. We introduce the ‘Cross’ Tapered Gridded Estimator (Cross TGE), which cross-correlates two cross-polarizations (RR and LL) to estimate the multifrequency angular power spectrum Cℓ(Δν). We expect this to mitigate several effects like noise bias, calibration errors, etc., which affect the ‘Total’ TGE that combines the two polarizations. Here, we apply the Cross TGE on $24.4 \text{-} \rm {MHz}$-bandwidth uGMRT (upgraded Giant Metrewave Radio Telescope) Band 3 data centred at $432.8 \, \rm {MHz}$ aiming H i IM at z = 2.28. The measured Cℓ(Δν) is modelled to yield maximum likelihood estimates of the foregrounds and the spherical power spectrum P(k) in several k bins. Considering the mean squared brightness temperature fluctuations, we report a 2σ upper limit $\Delta _{\mathrm{ UL}}^{2}(k) \le (58.67)^{2} \, {\rm mK}^{2}$ at $k=0.804 \, {\rm Mpc}^{-1}$, which is a factor of 5.2 improvement on our previous estimate based on the Total TGE. Assuming that the H i traces the underlying matter distribution, we have modelled Cℓ(Δν) to simultaneously estimate the foregrounds and $[\Omega _{\rm{ H}\, {\small {I}}} b_{\rm{ H}\, {\small {I}}}]$, where $\Omega _{\rm{ H}\, {\small {I}}}$ and $b_{\rm{ H}\, {\small {I}}}$ are the H i density and linear bias parameters, respectively. We obtain a best-fitting value of $[\Omega _{\rm{ H}\, {\small {I}}}b_{\rm{ H}\, {\small {I}}}]^2 = 7.51\times 10^{-4} \pm 1.47\times 10^{-3}$ that is consistent with noise. Although the 2σ upper limit $[\Omega _{\rm{ H}\, {\small {I}}}b_{\rm{ H}\, {\small {I}}}]_{\mathrm{ UL}} \le 0.061$ is ∼50 times larger than the expected value, this is a considerable improvement over earlier works at this redshift.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| 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".