Measuring the baryon fraction using galaxy clustering
Why this work is in the frame
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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.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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 it