An IllustrisTNG view of the caustic technique for galaxy cluster mass estimation
Bibliographic record
Abstract
The TNG300-1 run of the IllustrisTNG simulations includes 1697 clusters of galaxies with M200c > 1014 M⊙ covering the redshift range 0.01 − 1.04. We built mock spectroscopic redshift catalogs of simulated galaxies within these clusters and applied the caustic technique to estimate the cumulative cluster mass profiles. We computed the total true cumulative mass profile from the 3D simulation data, calculated the ratio of caustic mass to total 3D mass as a function of cluster-centric distance, and identified the radial range where this mass ratio is roughly constant. The ratio of 3D to caustic mass on this plateau defines ℱβ. The filling factor, ℱβ = 0.41 ± 0.08, is constant on a plateau that covers a wide cluster-centric distance range, (0.6 − 4.2) R200c. This calibration is insensitive to redshift. The calibrated caustic mass profiles are unbiased, with an average uncertainty of 23%. At R200c, the average MC/M3D = 1.03 ± 0.22; at 2 R200c, the average MC/M3D = 1.02 ± 0.23. Simulated galaxies are unbiased tracers of the mass distribution. IllustrisTNG is a broad statistical platform for application of the caustic technique to large samples of clusters with spectroscopic redshifts for ≳200 members in each system. These observations will allow extensive comparisons with weak-lensing masses and will complement other techniques for measuring the growth rate of structure in 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".