Neutrino Mass Constraints from Reconstructing the Large-scale Structure: Systematic Uncertainty
Bibliographic record
Abstract
Abstract We examine the possibility of applying the baryonic acoustic oscillation reconstruction method to improve the neutrino mass Σm ν constraint. Thanks to the Gaussianization of the process, we demonstrate that the reconstruction algorithm could improve the measurement accuracy by roughly a factor of two. On the other hand, the reconstruction process itself becomes a source of systematic error. While the algorithm is supposed to produce the displacement field from a density distribution, various approximations cause the reconstructed output to deviate on intermediate scales. Nevertheless, it is still possible to benefit from this Gaussianized field, given that we can carefully calibrate the “transfer function” between the reconstruction output and theoretical displacement divergence from simulations. The limitation of this approach is then set by the numerical stability of this transfer function. With an ensemble of simulations, we show that such systematic error could become comparable to statistical uncertainties for a DESI-like survey and be safely neglected for other less ambitious surveys.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".