LENSING MAGNIFICATION: A NOVEL METHOD TO WEIGH HIGH-REDSHIFT CLUSTERS AND ITS APPLICATION TO SpARCS
Bibliographic record
Abstract
We introduce a novel method to measure the masses of galaxy clusters at high redshift selected from optical and IR Spitzer data via the red-sequence technique. Lyman-break galaxies are used as a well-understood, high-redshift background sample allowing mass measurements of lenses at unprecedented high redshifts using weak lensing magnification. By stacking a significant number of clusters at different redshifts with average masses of ∼(1–3) × 10 14 M ☉ , as estimated from their richness, we can calibrate the normalization of the mass–richness relation. With the current data set (area: 6 deg 2 ) we detect a magnification signal at the >3σ level. There is good agreement between the masses estimated from the richness of the clusters and the average masses estimated from magnification, albeit with large uncertainties. We perform tests that suggest the absence of strong systematic effects and support the robustness of the measurement. This method—when applied to larger data sets in the future—will yield an accurate calibration of the mass–observable relations at z ≳ 1 which will represent an invaluable input for cosmological studies using the galaxy cluster mass function and astrophysical studies of cluster formation. Furthermore, this method will probably be the least expensive way to measure masses of large numbers of z > 1 clusters detected in future IR-imaging surveys.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".