A Kinematic Approach to Assessing Environmental Effects: Star-forming Galaxies in a z ~ 0.9 SpARCS Cluster Using Spitzer 24 μm Observations
Bibliographic record
Abstract
We present an infrared study of a z = 0.872 cluster, SpARCS J161314+564930, with the primary aim of distinguishing the dynamical histories of spectroscopically confirmed star-forming members to assess the role of cluster environment. We utilize deep MIPS imaging and a mass-limited sample of 85 spectroscopic members to identify 16 24 {$μ$}m bright sources within the cluster, and measure their 24 {$μ$}m star formation rates (SFRs) down to ~{}6 M $_{⊙}$ yr$^{-1}$. Based on their line-of-sight velocities and stellar ages, MIPS cluster members appear to be an infalling population that was recently accreted from the field with minimal environmental dependency on their star formation. However, we identify a double-sequenced distribution of star-forming galaxies among the members, with one branch exhibiting declining specific SFRs with mass. The members along this sub-main sequence contain spectral features suggestive of passive galaxies. Using caustic diagrams, we kinematically identify these galaxies as a virialized and/or backsplash population. Moreover, we find a mix of dynamical histories at all projected radii, indicating that standard definitions of environment (i.e., radius and density) are contaminated with recently accreted interlopers, which could contribute to a lack of environmental trends for star-forming galaxies. A cleaner narrative of their dynamical past begins to unfold when using a proxy for accretion histories through profiles of constant (r/r $_{200}$) {\times} ({$Delta$}v/{$σ$}$_{ v }$); galaxies accreted at earlier times possess lower values of (r/r $_{200}$) {\times} ({$Delta$}v/{$σ$}$_{ v }$) with minimal contamination from the distinct infalling population. Therefore, adopting a time-averaged definition for density (as traced by accretion histories) rather than an instantaneous density yields a depressed specific SFR within the dynamical cluster core.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".