Precise weak lensing constraints from deep high-resolution <i>K</i><sub>s</sub> images: VLT/HAWK-I analysis of the super-massive galaxy cluster RCS2 J 232727.7−020437 at <i>z</i> = 0.70
Bibliographic record
Abstract
We demonstrate that deep good-seeing VLT/HAWK-I Ks images complemented with g + z-band photometry can yield a sensitivity for weak lensing studies of massive galaxy clusters at redshifts 0.7 ≲ z ≲ 1.1, which is almost identical to the sensitivity of HST/ACS mosaics of single-orbit depth. Key reasons for this good performance are the excellent image quality frequently achievable for Ks imaging from the ground, a highly effective photometric selection of background galaxies, and a galaxy ellipticity dispersion that is noticeably lower than for optically observed high-redshift galaxy samples. Incorporating results from the 3D-HST and UltraVISTA surveys we also obtained a more accurate calibration of the source redshift distribution than previously achieved for similar optical weak lensing data sets. Here we studied the extremely massive galaxy cluster RCS2 J232727.7−020437 (z = 0.699), combining deep VLT/HAWK-I Ks images (point spread function with a 0.′′35 full width at half maximum) with LBT/LBC photometry. The resulting weak lensing mass reconstruction suggests that the cluster consists of a single overdensity, which is detected with a peak significance of 10.1σ. We constrained the cluster mass to M200c/(1015 M⊙) = 2.06−0.26+0.28(stat.) ± 0.12(sys.) assuming a spherical Navarro, Frenk & White model and simulation-based priors on the concentration, making it one of the most massive galaxy clusters known in the z ≳ 0.7 Universe. We also cross-checked the HAWK-I measurements through an analysis of overlapping HST/ACS images, yielding fully consistent estimates of the lensing signal.
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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 | 0.000 |
| 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".