Resolved stellar mass maps of galaxies in the Hubble Frontier Fields : evidence for mass dependency in environmental quenching
Bibliographic record
Abstract
One of the challenges in understanding the quenching processes for galaxies is connecting progenitor star-forming populations to their descendant quiescent populations over cosmic time. Here we attempt a novel approach to this challenge by assuming that the underlying stellar mass distribution of galaxies is not significantly altered during environmental-quenching processes that solely affect the gas content of cluster galaxies, such as strangulation and ram pressure stripping. Using the deep, high-resolution photometry of the Hubble Frontier Fields, we create resolved stellar mass maps for both cluster and field galaxies, from which we determine 2D Sérsic profiles, and obtain Sérsic indices and half-mass radii. We classify the quiescent cluster galaxies into disk-like and bulge-like populations based on their Sérsic indices, and find that bulge-like quiescent galaxies dominate the quiescent population at higher masses (M⋆ > 10⁹.⁵M⊙), whereas disk-like quiescent galaxies dominate at lower masses (10⁸.⁵M⊙ < M⋆ < 10⁹.⁵M⊙). Using both the Sérsic indices and half-mass radii, we identify a population of quiescent galaxies in clusters that are morphological analogs of field star-forming galaxies. These analogs are interpreted to be star-forming galaxies that had been environmentally quenched. We use these morphological analogs to compute the environmental-quenching efficiency, and we find that the efficiency decreases with increasing stellar mass. This demonstrates that environmental quenching is more effective on less massive galaxies and that the effect of environment on quenching galaxies is not completely separable from the effect of mass on quenching 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".