The JWST Resolved Stellar Populations Early Release Science Program. II. Survey Overview
Bibliographic record
Abstract
Abstract We present the JWST Resolved Stellar Populations Early Release Science (ERS) program. We obtained 27.5 hr of NIRCam and NIRISS imaging of three targets in the Local Group (Milky Way globular cluster M92, ultrafaint dwarf galaxy Draco II , and star-forming dwarf galaxy WLM), which span factors of ∼10 5 in luminosity, ∼10 4 in distance, and ∼10 5 in surface brightness. We describe the survey strategy, scientific and technical goals, implementation details, present select NIRCam color–magnitude diagrams (CMDs), and validate the NIRCam exposure time calculator (ETC). Our CMDs are among the deepest in existence for each class of target. They touch the theoretical hydrogen-burning limit in M92 (<0.08 M ⊙ ; M F090W ∼ +13.6), include the lowest-mass stars observed outside the Milky Way in Draco II (0.09 M ⊙ ; M F090W ∼ +12.1), and reach ∼1.5 mag below the oldest main-sequence turnoff in WLM ( M F090W ∼ +4.6). The PARSEC stellar models provide a good qualitative match to the NIRCam CMDs, though they are ∼0.05 mag too blue compared to M92 F090W − F150W data. Our CMDs show detector-dependent color offsets ranging from ∼0.02 mag in F090W – F150W to ∼0.1 mag in F277W – F444W; these appear to be due to differences in the zero-point calibrations among the detectors. The NIRCam ETC (v2.0) matches the signal-to-noise ratios based on photon noise in uncrowded fields, but the ETC may not be accurate in more crowded fields, similar to what is known for the Hubble Space Telescope. We release the point-source photometry package DOLPHOT, optimized for NIRCam and NIRISS, for the community.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".