Cosmological constraints from the nonlinear galaxy bispectrum
Bibliographic record
Abstract
We present the cosmological constraints from analyzing higher-order galaxy clustering on small nonlinear scales. We use SimBIG, a forward modeling framework for galaxy clustering analyses that employs simulation-based inference to perform highly efficient cosmological inference using normalizing flows. It leverages the predictive power of high-fidelity simulations and robustly extracts cosmological information from regimes inaccessible with current standard analyses. In this work, we apply SimBIG to a subset of the BOSS galaxy sample and analyze the redshift-space bispectrum monopole, ${B}_{0}({k}_{1},{k}_{2},{k}_{3})$, to ${k}_{\mathrm{max}}=0.5\text{ }\text{ }h/\mathrm{Mpc}$. We achieve $1\ensuremath{\sigma}$ constraints of ${\mathrm{\ensuremath{\Omega}}}_{m}=0.29{3}_{\ensuremath{-}0.027}^{+0.027}$ and ${\ensuremath{\sigma}}_{8}=0.78{3}_{\ensuremath{-}0.038}^{+0.040}$, which are more than 1.2 and $2.4\ifmmode\times\else\texttimes\fi{}$ tighter than constraints from standard power spectrum analyses of the same dataset. We also derive 1.4, 1.4, $1.7\ifmmode\times\else\texttimes\fi{}$ tighter constraints on ${\mathrm{\ensuremath{\Omega}}}_{b}$, $h$, ${n}_{s}$. This improvement comes from additional cosmological information in higher-order clustering on nonlinear scales and, for ${\ensuremath{\sigma}}_{8}$, is equivalent to the gain expected from a standard analysis on a $\ensuremath{\sim}4\ifmmode\times\else\texttimes\fi{}$ larger galaxy sample. Even with our BOSS subsample, which only spans 10% of the full BOSS volume, we derive competitive constraints on the growth of structure: ${S}_{8}=0.77{4}_{\ensuremath{-}0.053}^{+0.056}$. Our constraint is consistent with results from both cosmic microwave background and weak lensing. Combined with a ${\ensuremath{\omega}}_{b}$ prior from big bang nucleosynthesis, we also derive a constraint on ${H}_{0}={67.6}_{\ensuremath{-}1.8}^{+2.2}\text{ }\text{ }\mathrm{km}\text{ }{\mathrm{s}}^{\ensuremath{-}1}\text{ }{\mathrm{Mpc}}^{\ensuremath{-}1}$ that is consistent with early Universe constraints.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".