Retrieving cosmological information from small-scale CMB foregrounds
Bibliographic record
Abstract
Recent results of ground-based telescopes, giving high-quality measurements of the cosmic microwave background (CMB) temperature power spectrum on small scales (below 1 deg) motivate the need for an accurate model of foregrounds, which dominate the primary signal at these multipoles. In a previous work, we have shown that cosmological information could be retrieved from the power spectrum of the thermal Sunyaev Zel’dovich (SZ) effect. In this work, we introduce a physically motivated model of the Epoch of Reionisation in the cosmological analysis of CMB data, which is coherent on all scales. In particular, at high multipoles, the power spectrum of the kinetic SZ (kSZ) effect is inferred from a set of cosmological and reionisation parameters by a machine-learning algorithm. First including an asymmetric parameterisation of the reionisation history in thePlanck2018 data analysis, we retrieve a value of the Thomson optical depth consistent with previous results, but stemming from a completely different history of reionisation in which the first luminous sources light up as early asz = 15. Considering the latest small-scale data from the South Pole telescope (SPT) and letting the cosmology free to vary, we find that including the new cosmology-dependent tSZ and kSZ spectra helps tighten the constraints on their amplitudes by breaking their degeneracy. We report a 5σmeasurement of the kSZ signal atℓ = 3000, D3000kSZ= 3.4−0.3+0.5μK2at the 68% confidence level (C.L.), marginalised over cosmology, as well as an upper limit on the patchy signal from reionisation D3000pkSZ< 1.6 μK2(95% C.L.). Additionally, we find that the SPT data favour slightly earlier reionisation scenarios thanPlanck, leading toτ= 0.062−0.015+0.012and a reionisation midpointzre= 7.9−1.3+1.1(68% C.L.), which is in line with constraints from high-redshift quasars and galaxies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".