The extended Baryon Oscillation Spectroscopic Survey: a cosmological forecast
Bibliographic record
Abstract
We present a science forecast for the extended Baryon Oscillation Spectroscopic Survey (eBOSS) survey. Focusing on discrete tracers, we forecast the expected accuracy of the baryonic acoustic oscillation (BAO), the redshift-space distortion (RSD) measurements, the fNL parameter quantifying the primordial non-Gaussianity, the dark energy and modified gravity parameters. We also use the line-of-sight clustering in the Lyman α forest to constrain the total neutrino mass. We find that eBOSS luminous red galaxies, emission line galaxies and clustering quasars can achieve a precision of 1, 2.2 and 1.6 per cent, respectively, for spherically averaged BAO distance measurements. Using the same samples, the constraint on fσ8 is expected to be 2.5, 3.3 and 2.8 per cent, respectively. For primordial non-Gaussianity, eBOSS alone can reach an accuracy of σ(fNL) ∼ 10–15. eBOSS can at most improve the dark energy figure of merit by a factor of 3 for the Chevallier–Polarski–Linder parametrization, and can well constrain three eigenmodes for the general equation-of-state parameter. eBOSS can also significantly improve constraints on modified gravity parameters by providing the RSD information, which is highly complementary to constraints obtained from weak lensing measurements. A principal component analysis shows that eBOSS can measure the eigenmodes of the effective Newton's constant to 2 per cent precision; this is a factor of 10 improvement over that achievable without eBOSS. Finally, we derive the eBOSS constraint (combined with Planck, Dark Energy Survey and BOSS) on the total neutrino mass, σ(Σmν) = 0.03 eV (68 per cent CL), which in principle makes it possible to distinguish between the two scenarios of neutrino mass hierarchies.
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 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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".