Time-delay cosmographic forecasts with strong lensing and JWST stellar kinematics
Bibliographic record
Abstract
ABSTRACT We present a joint strong lensing and stellar dynamical framework for future time-delay cosmography purposes. Based on a pixelated source reconstruction and the axisymmetric Jeans equations, we are capable of constraining cosmological distances and hence the current expansion rate of the Universe (H0) to the few per cent level per lens, when high signal-to-noise integral field unit (IFU) observations from the next generation of telescopes become available. For illustrating the power of this method, we mock up IFU stellar kinematic data of the prominent lens system RXJ1131−1231, given the specifications of the James Webb Space Telescope. Our analysis shows that the time-delay distance (DΔt) can be constrained with 3.1 per cent uncertainty at best, if future IFU stellar kinematics are included in the fit and if the set of candidate model parametrizations contains the true lens potential. These constraints would translate to a 3.2 per cent precision measurement on H0 in flat ΛCDM cosmology from the single lens RXJ1131−1231, and can be expected to yield an H0 measure with ≤2.0 per cent uncertainty, if similar gains in precision can be reached for two additional lens systems. Moreover, the angular diameter distance (Dd) to RXJ1131−1231 can be constrained with 2.4 per cent precision, providing two distance measurements from a single lens system, which is extremely powerful to further constrain the matter density (Ωm). The measurement accuracy of Dd, however, is highly sensitive to any systematics in the measurement of the stellar kinematics. For both distance measurements, we strongly advise to probe a large set of physically motivated lens potentials in the future, to minimize the systematic errors associated with the lens mass parametrization.
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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 | 0.002 | 0.011 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".