SEMI-BLIND EIGEN ANALYSES OF RECOMBINATION HISTORIES USING COSMIC MICROWAVE BACKGROUND DATA
Bibliographic record
Abstract
Cosmological parameter measurements from cosmic microwave background (CMB) experiments, such as Planck, ACTPol, SPTPol, and other high-resolution follow-ons, fundamentally rely on the accuracy of the assumed recombination model or one with well-prescribed uncertainties.Deviations from the standard recombination history might suggest new particle physics or modified atomic physics.Here we treat possible perturbative fluctuations in the free electron fraction, X e (z), by a semi-blind expansion in densely packed modes in redshift.From these we construct parameter eigenmodes, which we rank order so that the lowest modes provide the most power to probe X e (z) with CMB measurements.Since the eigenmodes are effectively weighed by the fiducial X e history, they are localized around the differential visibility peak, allowing for an excellent probe of hydrogen recombination but a weaker probe of the higher redshift helium recombination and the lower redshift highly neutral freezeout tail.We use an information-based criterion to truncate the mode hierarchy and show that with even a few modes the method goes a long way from the fiducial recombination model computed with Recfast, X e,i (z), toward the precise underlying history given by the new and improved recombination calculations of CosmoRec or HyRec, X e,f (z), in the hydrogen recombination regime, though not well in the helium regime.Without such a correction, the derived cosmic parameters are biased.We discuss an iterative approach for updating the eigenmodes to further hone in on X e,f (z) if large deviations are indeed found.We also introduce control parameters that downweight the attention on the visibility peak structure, e.g., focusing the eigenmode probes more strongly on the X e (z) freezeout tail, as would be appropriate when looking for the X e signature of annihilating or decaying elementary particles.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".