MODELING EXTRAGALACTIC FOREGROUNDS AND SECONDARIES FOR UNBIASED ESTIMATION OF COSMOLOGICAL PARAMETERS FROM PRIMARY COSMIC MICROWAVE BACKGROUND ANISOTROPY
Bibliographic record
Abstract
Using the latest physical modeling and constrained by the most recent data, we develop a phenomenological parameterized model of the contributions to intensity and polarization maps at millimeter wavelengths from external galaxies and Sunyaev–Zeldovich effects. We find such modeling to be necessary for estimation of cosmological parameters from Planck data. For example, ignoring the clustering of the infrared background would result in a bias in n s of 7σ in the context of an eight-parameter cosmological model. We show that the simultaneous marginalization over a full foreground model can eliminate such biases, while increasing the statistical uncertainty in cosmological parameters by less than 20%. The small increases in uncertainty can be significantly reduced with the inclusion of higher-resolution ground-based data. The multi-frequency analysis we employ involves modeling 46 total power spectra and marginalization over 17 foreground parameters. We show that we can also reduce the data to a best estimate of the cosmic microwave background power spectra, with just two principal components (with constrained amplitudes) describing residual foreground contamination.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".