Constraining <i>M</i><sub>ν</sub> with the bispectrum. Part I. Breaking parameter degeneracies
Bibliographic record
Abstract
Massive neutrinos suppress the growth of structure below their free-streaming scale and leave an imprint on large-scale structure. Measuring this imprint allows us to constrain the sum of neutrino masses, M ν , a key parameter in particle physics beyond the Standard Model. However, degeneracies among cosmological parameters, especially between M ν and σ 8 , limit the constraining power of standard two-point clustering statistics. In this work, we investigate whether we can break these degeneracies and constrain M ν with the next higher-order correlation function—the bispectrum. We first examine the redshift-space halo bispectrum of 800 N -body simulations from the HADES suite and demonstrate that the bispectrum helps break the M ν -σ 8 degeneracy. Then using 22,000 N -body simulations of the Quijote suite, we quantify for the first time the full information content of the redshift-space halo bispectrum down to nonlinear scales using a Fisher matrix forecast of {Ω m , Ω b , h , n s , σ 8 , M ν }. For k max =0.5 H /Mpc, the bispectrum provides Ω m , Ω b , h , n s , and σ 8 constraints 1.9, 2.6, 3.1, 3.6, and 2.6 times tighter than the power spectrum. For M ν , the bispectrum improves the 1σ constraint from 0.2968 to 0.0572 eV—over 5 times tighter than the power spectrum. Even with priors from Planck , the bispectrum improves M ν constraints by a factor of 1.8. Although we reserve marginalizing over a more complete set of bias parameters to the next paper of the series, these constraints are derived for a (1 h −1 Gpc) 3 box, a substantially smaller volume than upcoming surveys. Thus, our results demonstrate that the bispectrum offers significant improvements over the power spectrum, especially for constraining M ν .
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.001 | 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".