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Record W4293150437 · doi:10.1093/mnras/stac2419

Towards 21-cm intensity mapping at <i>z</i> = 2.28 with uGMRT using the tapered gridded estimator I: Foreground avoidance

2022· article· en· W4293150437 on OpenAlexaff
Srijita Pal, Khandakar Md Asif Elahi, Somnath Bharadwaj, Sk. Saiyad Ali, Samir Choudhuri, Abhik Ghosh, Arnab Chakraborty, Abhirup Datta, Nirupam Roy, Madhurima Choudhury, Prasun Dutta

Bibliographic record

VenueMonthly Notices of the Royal Astronomical Society · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicRadio Astronomy Observations and Technology
Canadian institutionsMcGill University
FundersDepartment of Science and Technology, Government of West BengalScience and Engineering Research BoardTata Institute of Fundamental Research
KeywordsPhysicsReionizationRedshiftOmegaSpectral densityEstimatorAstrophysicsBrightness temperatureIntensity mappingIntensity (physics)SkyField (mathematics)BrightnessOpticsStatisticsGalaxyMathematicsQuantum mechanics

Abstract

fetched live from OpenAlex

ABSTRACT The post-reionization (z ≤ 6) neutral hydrogen (H i) 21-cm intensity mapping signal holds the potential to probe the large-scale structures, study the expansion history, and constrain various cosmological parameters. Here, we apply the Tapered Gridded Estimator (TGE) to estimate P(k⊥, k∥) the power spectrum of the $z = 2.28\, (432.8\, {\rm MHz})$ redshifted 21-cm signal using a $24.4\, {\rm MHz}$ sub-band drawn from uGMRT Band 3 observations of European Large-Area ISO Survey-North 1 (ELAIS-N1). The TGE allows us to taper the sky response, which suppresses the foreground contribution from sources in the periphery of the telescope’s field of view. We apply the TGE on the measured visibility data to estimate the multifrequency angular power spectrum (MAPS) Cℓ(Δν) from which we determine P(k⊥, k∥) using maximum likelihood that naturally overcomes the issue of missing frequency channels (55 per cent here). The entire methodology is validated using simulations. For the data, using the foreground avoidance technique, we obtain a $2\, \sigma$ upper limit of $\Delta ^2(k) \le (133.97)^2 \, {\rm mK}^{2}$ for the 21-cm brightness temperature fluctuation at $k = 0.347 \, \textrm {Mpc}^{-1}$. This corresponds to $[\Omega _{\rm H\, {\small I}~}b_{\rm H\, {\small I}~}] \le 0.23$, where $\Omega _{\rm H\, {\small I}~}$ and $b_{\rm H\, {\small I}~}$, respectively, denote the cosmic H i mass density and the H i bias parameter. A previous work has analysed $8 \, {\rm MHz}$ of the same data at z = 2.19, and reported $\Delta ^{2}(k) \le (61.49)^{2} \, {\rm mK}^{2}$ and $[\Omega _{\rm H\, {\small I}~} b_{\rm H\, {\small I}~}] \le 0.11$ at $k=1 \, {\rm Mpc}^{-1}$. The upper limits presented here are still orders of magnitude larger than the expected signal corresponding to $\Omega _{\rm H\, {\small I}~} \sim 10^{-3}$ and $b_{\rm H\, {\small I}~} \sim 2$.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.204
Teacher spread0.189 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2022
Admission routes1
Has abstractyes

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