<i>Euclid</i> : Early Release Observations – A combined strong and weak lensing solution for Abell 2390 beyond its virial radius
Bibliographic record
Abstract
Euclid is currently mapping the distribution of matter in the Universe in detail via the weak lensing signature of billions of distant galaxies. The weak lensing signal is most prominent around galaxy clusters and can extend up to distances well beyond their virial radius, thus constraining their total mass. Near the centre of clusters, where contamination by member galaxies is an issue, the weak lensing data can be complemented with strong lensing data. Strong lensing information can also diminish the uncertainty due to the mass-sheet degeneracy and provide high-resolution information about the distribution of matter in the centre of clusters. Here we present a joint strong and weak lensing analysis of the Euclid Early Release Observations of the cluster Abell 2390 at z = 0.228. Thanks to Euclid ’s wide field of view of 0.5 deg 2 , combined with its angular resolution in the visible band of 0.″13 and sampling of 0.″1 per pixel, we constrained the density profile in a wide range of radii, 30 kpc < r < 2000 kpc, from the inner region near the brightest cluster galaxy to beyond the virial radius of the cluster. We find relatively good consistency with earlier X-ray results based on assumptions of hydrostatic equilibrium and thus indirectly confirm the nearly relaxed state of this cluster. We also find consistency with previous results based on weak lensing data and ground-based observations of this cluster. From the combined strong + weak lensing profile, we derive the values of the viral mass M 200 = (1.48 ± 0.29) × 10 15 M ⊙ , and virial radius r 200 = (2.05 ± 0.13 Mpc), with error bars representing one standard deviation. The profile is well described by a Navarro-Frenk-White model with a concentration c = 6.5 and a small-scale radius of 230 kpc in the 30 kpc < r < 2000 kpc range that is best constrained by strong lensing and weak lensing data. Abell 2390 is the first of many examples where Euclid data will play a crucial role in providing masses for clusters. The large coverage provided by Euclid , combined with the depth of the observations and high angular resolution, will allow us to produce similar results in hundreds of other clusters with rich strong lensing data that is already available.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".