Measuring the baryon fraction using galaxy clustering
Bibliographic record
Abstract
The amplitude of the baryon signature in galaxy clustering depends on the cosmological baryon fraction. We consider two ways to isolate this signal in galaxy redshift surveys. First, we extend standard template-based Baryon Acoustic Oscillation (BAO) models to include the amplitude of the baryonic signature by splitting the transfer function into baryon and cold dark matter components with freely varying proportions. Second, we include the amplitude of the split as an extra parameter in Effective Field Theory of Large Scale Structure (EFT) models of the full galaxy clustering signal. We find similar results from both approaches. For the Baryon Oscillation Spectroscopic Survey (BOSS) data we find $f_b\equivΩ_b/Ω_m=0.173\pm0.027$ for template fits post-reconstruction, $f_b=0.153\pm0.029$ for template fits pre-reconstruction, and $f_b=0.154\pm0.022$ for EFT fits, with an estimated systematic error of 0.013 for all three methods. Using reconstruction only produces a marginal improvement for these measurements. Although significantly weaker than constraints on $f_b$ from the Cosmic Microwave Background, these measurements rely on very simple physics and, in particular, are independent of the sound horizon. In a companion paper we show how they can be used, together with Big Bang Nucleosynthesis measurements of the physical baryon density and geometrical measurements of the matter density from the Alcock-Paczynski effect, to constrain the Hubble parameter. While the constraints on $H_0$ based on density measurements from BOSS are relatively weak, measurements from DESI and Euclid will lead to errors on $H_0$ that are competitive with those from local distance ladder measurements.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".