Galaxy cluster mass accretion rates from IllustrisTNG
Bibliographic record
Abstract
We used simulated cluster member galaxies from the TNG300-1 run of the IllustrisTNG simulations to develop a technique for measuring the galaxy cluster mass accretion rate (MAR) that can be applied directly to observations. We analyzed 1318 IllustrisTNG clusters of galaxies with M200c > 1014 M⊙ and 0.01 ≤ z ≤ 1.04. The MAR we derived is the ratio between the mass of a spherical shell located in the infall region and the time for the infalling shell to accrete onto the virialized region of the cluster. At fixed redshift, an approximately one order of magnitude increase in M200c results in a comparable increase in MAR. At fixed mass, the MAR increases by a factor of approximately five from z = 0.01 to z = 1.04. The MAR estimates derived from the caustic technique are unbiased and lie within 20% of the MARs based on the true mass profiles. This agreement is crucial for observational derivation of the MAR. The IllustrisTNG results are also consistent with (i) previous merger tree approaches based on N-body dark matter only simulations and with (ii) previously determined MARs of real clusters based on the caustic method. Future spectroscopic and photometric surveys will provide MARs of enormous cluster samples with mass profiles derived from both spectroscopy and weak lensing. Combined with future larger volume hydrodynamical simulations that extend to higher redshift, the MAR promises important insights into the evolution of massive systems of galaxies.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".