Theoretical estimates of integrated Sachs–Wolfe effect detection through the Australian Square Kilometre Array Pathfinder’s Evolutionary Map of the Universe (ASKAP- EMU) survey, with confusion, position uncertainty, shot noise, and signal-to-noise ratio analysis
Bibliographic record
Abstract
Detection of the late-time integrated Sachs–Wolfe (ISW) effect is an active area of study related to large-scale structures (LSSs). The ISW effect can be studied by observing the non-zero cross-correlation between cosmic microwave background (CMB) anisotropies with tracers of mass field, such as galaxy survey data. We study this effect by cross-correlating the CMB data and related cosmological parameters, as delineated by the Wilkinson Microwave Anisotropy Probe (WMAP), with the upcoming Evolutionary Map of the Universe (EMU) survey planned for the Australian Square Kilometre Array Pathfinder (ASKAP). ASKAP-EMU will conduct a deep radio continuum survey with a root-mean-square (rms) flux of 10 μJy per beam (1 Jy = 10–26 Wm–2Hz–1). The survey will cover the entire southern sky, extending to +30° declination. To infer the expected redshift distribution (dN/dz) and differential source count (S) that can be extracted from the galaxies surveyed via EMU, we use data from the S-cubed simulation of extragalactic radio continuum sources (S3-SEX) for the Square Kilometre Array Design Studies (SKADS). We also calculate various parameters including galaxy survey shot noise, root mean square confusion uncertainty, and position uncertainty for the survey, which can help in understanding the accuracy of the survey results and in performing the data analysis. We also discuss signal-to-noise ratios over a range of maximum redshifts and maximum multipole values with some discussion on constraints over dark energy density parameter (ΩΛ) and baryonic matter density parameter (Ωb).
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".