Combining Strong and Weak Gravitational Lensing in Abell 1689
Bibliographic record
Abstract
We present a reconstruction of the mass distribution of galaxy cluster Abell 1689 at z = 0.18 using detected strong lensing features from deep ACS observations and extensive ground based spectroscopy. Earlier analyses have reported up to 32 multiply imaged systems in this cluster, of which only 3 were spectroscopically confirmed. In this work, we present a parametric strong lensing mass reconstruction using 34 multiply imaged systems of which 24 have newly determined spectroscopic redshifts, which is a major step forward in building a robust mass model. In turn, the new spectroscopic data allows a more secure identification of multiply imaged systems. The resultant mass model enables us to reliably predict the redshifts of additional multiply imaged systems for which no spectra are currently available, and to use the location of these systems to further constrain the mass model. Using our strong lensing mass model, we predict on larger scale a shear signal which is consistent with that inferred from our large scale weak lensing analysis derived using CFH12K wide field images. Thanks to a new method for reliably selecting a well defined background lensed galaxy population, we resolve the discrepancy found between the NFW concentration parameters derived from earlier strong and weak lensing analysis. The derived parameters for the best fit NFW profile is found to be c 200 = 7.6 ± 1.6 and r 200 = 2.16 ± 0.10 h Mpc (corresponding to a 3D mass equal to M 200 = [1.32 ± 0.2] × 10 15 h 70 M ☉ ). The large number of new constraints incorporated in this work makes Abell 1689 the most reliably reconstructed cluster to date. This well calibrated mass model, which we here make publicly available, will enable us to exploit Abell 1689 efficiently as a gravitational telescope, as well as to potentially constrain cosmology.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".