Magellanic System Stars Identified in SMACS J0723.3-7327 James Webb Space Telescope Early Release Observations Images
Bibliographic record
Abstract
Abstract We identify 71 distant stars in James Webb Space Telescope/NIRCam early release observations (ERO) images of the field of galaxy cluster SMACS J0723.3-7327 (SMACS 0723). Given the relatively small (∼10°) angular separation between SMACS 0723 and the Large Magellanic Cloud (LMC), it is likely that these stars are associated with the LMC outskirts or the Leading Arm. This is further bolstered by a spectral energy distribution (SED) analysis, which suggests an excess of stars at a physical distance of 40–100 kpc, consistent with being associated with or located behind the Magellanic system. In particular, we find that the overall surface density of stars brighter than 27.0 mag in the field of SMACS 0723 is ∼2.3 times that of stars in a blank field with similar Galactic latitude (the North Ecliptic Pole Time Domain Field), and that the density of stars in the SMACS 0723 field with SED-derived distances consistent with the Magellanic system is ∼6.1 times larger than that of the blank field. The candidate stars at these distances are consistent with a stellar population at the same distance modulus with [Fe/H] = −1.0 and an age of ∼5.0 Gyr. On the assumption that all of the 71 stars are associated with the LMC, then the stellar density of the LMC at the location of the SMACS 0723 field is ∼740 stars kpc−3, which helps trace the density of stars in the LMC outskirts.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".