Modelling the BOSS void-galaxy cross-correlation function using a neural-network emulator
Bibliographic record
Abstract
Abstract We introduce an emulator-based method to model the cross-correlation between cosmological voids and galaxies. This allows us to model the effect of cosmology on void finding and on the shape of the void-galaxy cross-correlation function, improving on previous template-based methods. We train a neural network using the AbacusSummit simulation suite and fit to data from the Sloan Digital Sky Survey Baryon Oscillation Spectroscopic Survey sample. We recover information on the growth of structure through redshift-space distortions (RSD), and the geometry of the Universe through the Alcock-Paczyński (AP) effect, measuring Ω m = 0.330 ± 0.020 and σ 8 = 0.777 +0.047 -0.062 for a ΛCDM cosmology. Comparing to results from a template-based method, we find that fitting the shape of the void-galaxy cross-correlation function provides more information and leads to an improvement in constraining power. In contrast, we find that errors on the AP measurements were previously underestimated if void centres were assumed to have the same response to the AP effect as galaxies — a common simplification. Overall, we recover a 28% reduction in errors for Ω 8 and similar errors on σ 8 with our new method. Given the statistical power of future surveys including DESI and Euclid, we expect the method presented to become the new baseline for the analysis of voids in these data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".