Dark energy from large-scale structure lensing information
Bibliographic record
Abstract
Wide area large-scale structure (LSS) surveys are planning to map a substantial fraction of the visible Universe to quantify dark energy through baryon acoustic oscillations. At increasing redshift, for example, that probed by proposed 21-cm intensity mapping surveys, gravitational lensing potentially limits the fidelity (Hui et al., 2007) because it distorts the apparent matter distribution. In this paper we show that these distortions can be reconstructed, and actually used to map the distribution of intervening dark matter. The lensing information for sources at $z=1\ensuremath{-}3$ allows accurate reconstruction of the gravitational potential on large scales, $l\ensuremath{\lesssim}100$, which is well matched for integrated Sachs-Wolfe effect measurements of dark energy and its sound speed, and a strong constraint for modified gravity models of dark energy. We built an optimal quadratic lensing estimator for non-Gaussian sources, which is necessary for LSS. The phenomenon of ``information saturation'' (Rimes Hamilton, 2005) saturates reconstruction at mildly nonlinear scales, where the linear source power spectrum ${\ensuremath{\Delta}}^{2}\ensuremath{\sim}0.2--0.5$, depending on power spectrum slope. Naive Gaussian estimators with nonlinear cutoff can be tuned to reproduce the optimal non-Gaussian errors within a factor of 2. We compute the effective number densities of independent lensing sources for LSS lensing, and find that they increase rapidly with redshifts. For LSS/21-cm sources at $z\ensuremath{\sim}2--4$, the lensing reconstruction is limited by cosmic variance at $l\ensuremath{\lesssim}100$.
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 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 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".