ZFIRE: The Beginning of the End for Massive Galaxies at z ∼ 2 and Why Environment Matters
Bibliographic record
Abstract
Abstract We use ZFIRE and ZFOURGE observations with the spectral energy distribution fitting tool PROSPECTOR to reconstruct the star formation histories (SFHs) of protocluster and field galaxies at z ∼ 2 and compare our results to the TNG100 run of the IllustrisTNG cosmological simulation suite. In the observations, we find that massive protocluster galaxies ( <?CDATA $\mathrm{log}[{M}_{* }/{M}_{\odot }]$?> log [ M * / M ⊙ ] > 10.5) form 45% ± 8% of their total stellar mass in the first 2 Gyr of the universe, compared to 31% ± 2% formed in the field galaxies. In both observations and simulations, massive protocluster galaxies have a flat/declining SFH with decreasing redshift compared to rising SFH in their field counterparts. Using IllustrisTNG, we find that massive galaxies ( <?CDATA $\mathrm{log}[{M}_{* }/{M}_{\odot }]\geqslant 10.5$?> log [ M * / M ⊙ ] ≥ 10.5 ) in both environments are on average ≈190 Myr older than low-mass galaxies ( <?CDATA $\mathrm{log}[{M}_{* }/{M}_{\odot }]=9\mbox{--}9.5$?> log [ M * / M ⊙ ] = 9 – 9.5 ). However, the difference in mean stellar ages of cluster and field galaxies is minimal when considering the full range in stellar mass ( <?CDATA $\mathrm{log}[{M}_{* }/{M}_{\odot }]\geqslant 9$?> log [ M * / M ⊙ ] ≥ 9 ). We explore the role of mergers in driving the SFH in IllustrisTNG and find that massive cluster galaxies consistently experience mergers with low gas fraction compared to other galaxies after 1 Gyr from the big bang. We hypothesize that the low gas fraction in the progenitors of massive cluster galaxies is responsible for the reduced star formation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".