Weak‐Lensing Measurements of 42 SDSS/RASS Galaxy Clusters
Bibliographic record
Abstract
We present a lensing study of 42 galaxy clusters imaged in Sloan Digital Sky Survey (SDSS) commissioning data. Cluster candidates are selected optically from SDSS imaging data and confirmed for this study by matching to X-ray sources found independently in the ROSAT All-Sky Survey (RASS). Five-color SDSS photometry is used to make accurate (Δ z = 0.018) photometric redshift estimates that are used to rescale and combine the lensing measurements. The mean shear from these clusters is detected to 2 h -1 Mpc at the 7 σ level, corresponding to a mass within that radius of (4.2 ± 0.6) × 10 14 h -1 M ☉ . The shear profile is well fitted by a power law with index -0.9 ± 0.3, consistent with that of an isothermal density profile. Clusters are divided by X-ray luminosity into two subsets, with mean L X of (0.14 ± 0.03) × 10 44 and (1.0 ± 0.09) × 10 44 h -2 ergs s -1 . The average lensing signal is converted to a projected mass density based on fits to isothermal density profiles. From this we calculate a mean r 500 (the radius at which the mean density falls to 500 times the critical density) and M (< r 500 ). The mass contained within r 500 differs substantially between the low- and high- L X bins, with (0.7 ± 0.2) × 10 14 and 2.7 × 10 14 h -1 M ☉ , respectively. This paper demonstrates our ability to measure ensemble cluster masses from SDSS imaging data. The full SDSS data set will include ≳1000 SDSS/RASS clusters. With this large data set we will measure the M - L X relation with high precision and put direct constraints on the mass density of the universe.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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".