REQUIEM-2D Methodology: Spatially Resolved Stellar Populations of Massive Lensed Quiescent Galaxies from Hubble Space Telescope 2D Grism Spectroscopy
Bibliographic record
Abstract
Abstract We present a novel Bayesian methodology to jointly model photometry and deep Hubble Space Telescope 2D grism spectroscopy of high-redshift galaxies. Our requiem2d code measures both unresolved and resolved stellar populations, ages, and star formation histories (SFHs) for the ongoing REsolving QUIEscent Magnified (REQUIEM) Galaxies Survey, which targets strong gravitationally lensed quiescent galaxies at z ∼ 2. We test the accuracy of requiem2d using a simulated sample of massive galaxies at z ∼ 2 from the Illustris cosmological simulation and find that we recover the general trends in SFH and median stellar ages. We further present a pilot study for the REQUIEM Galaxies Survey: MRG-S0851, a quintuply imaged, massive ( ) red galaxy at z = 1.883 ± 0.001. With an estimated gravitational magnification of , we sample the stellar populations on 0.6 kpc physical size bins. The global mass-weighted median age is constrained to be Gyr, and our spatially resolved analysis reveals that MRG-S0851 has a flat age gradient in the inner 3 kpc core after taking into account the subtle effects of dust and metallicity on age measurements, favoring an early formation scenario. The analysis for the full REQUIEM-2D sample will be presented in a forthcoming paper with a beta release of the requiem2d code.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".