Efficient reconstruction of linear baryon acoustic oscillations in galaxy surveys
Bibliographic record
Abstract
Reconstructing an estimate of linear baryon acoustic oscillations (BAO) from an evolved galaxy field has become a standard technique in recent analyses. By partially removing non-linear damping caused by bulk motions, the real-space BAO peak in the correlation function is sharpened, and oscillations in the power spectrum are visible to smaller scales. In turn these lead to stronger measurements of the BAO scale. Future surveys are being designed assuming that this improvement has been applied, and this technique is therefore of critical importance for future BAO measurements. A number of reconstruction techniques are available, but the most widely used is a simple algorithm that decorrelates large-scale and small-scale modes approximately removing the bulk-flow displacements by moving the overdensity field. We consider the practical implementation of this algorithm, looking at the efficiency of reconstruction as a function of the assumptions made for the bulk-flow scale, the shot-noise level in a random catalogue used to quantify the mask and the method used to estimate the bulk-flow shifts. We also examine the efficiency of reconstruction against external factors including galaxy density, volume and edge effects, and consider their impact for future surveys. Throughout we make use of the mocks catalogues created for the Baryon Oscillation Spectroscopic Survey (BOSS) Date Release 11 samples covering 0.43 < z < 0.7 (CMASS) and 0.15 < z < 0.43 (LOWZ), to empirically test these changes.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| 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".