Testing dark energy with the Advanced Liquid-mirror Probe of Asteroids, Cosmology and Astrophysics
Bibliographic record
Abstract
The Advanced Liquid-mirror Probe of Asteroids, Cosmology and Astrophysics (ALPACA) is a proposed 8-m liquid-mirror telescope surveying ∼1000 deg2 of the Southern hemisphere sky. It will be a remarkably simple and inexpensive telescope that none the less will deliver a powerful sample of optical data for studying dark energy. The bulk of the cosmological data consist of nightly, high signal-to-noise ratio, multiband light curves of Type Ia supernovae (SNe Ia). At the end of the 3-yr run, ALPACA is expected to collect ≳100 000 SNe Ia up to z∼ 1. This will allow us to reduce present systematic uncertainties affecting the standard-candle relation. The survey will also provide several other data sets such as the detection of baryon acoustic oscillations in the matter power spectrum and shear weak-lensing measurements. In this preliminary analysis, we forecast constraints on dark energy parameters from SNe Ia and baryon acoustic oscillations. The combination of these two data sets will provide competitive constraints on the dark energy parameters under minimal prior assumptions. Further studies are needed to address the accuracy of weak-lensing measurements.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".