The Lyman-alpha forest power spectrum from the XQ-100 Legacy Survey
Bibliographic record
Abstract
We present the Lyman α flux power spectrum measurements of the XQ-100 sample of quasar spectra obtained in the context of the European Southern Observatory Large Programme ‘Quasars and their absorption lines: a legacy survey of the high redshift universe with VLT/XSHOOTER’. Using 100 quasar spectra with medium resolution and signal-to-noise ratio, we measure the power spectrum over a range of redshifts z = 3–4.2 and over a range of scales k = 0.003–0.06 km−1 s. The results agree well with the measurements of the one-dimensional power spectrum found in the literature. The data analysis used in this paper is based on the Fourier transform and has been tested on synthetic data. Systematic and statistical uncertainties of our measurements are estimated, with a total error (statistical and systematic) comparable to the one of the BOSS data in the overlapping range of scales, and smaller by more than 50 per cent for higher redshift bins (z > 3.6) and small scales (k > 0.01 km−1 s). The XQ-100 data set has the unique feature of having signal-to-noise ratios and resolution intermediate between the two data sets that are typically used to perform cosmological studies, i.e. BOSS and high-resolution spectra (e.g. UVES/VLT or HIRES). More importantly, the measured flux power spectra span the high-redshift regime that is usually more constraining for structure formation models.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".