JWST’s PEARLS: Mothra, a new kaiju star at <i>z</i> = 2.091 extremely magnified by MACS0416, and implications for dark matter models
Bibliographic record
Abstract
We report the discovery of Mothra, an extremely magnified monster star, likely a binary system of two supergiant stars, in one of the strongly lensed galaxies behind the galaxy cluster MACS J0416.1−2403. Mothra is in a galaxy with spectroscopic redshift z = 2.091 in a portion of the galaxy that is parsecs away from the cluster caustic. The binary star is observed only on the side of the critical curve with negative parity but has been detectable for at least eight years, implying the presence of a small lensing perturber. Microlenses alone cannot explain the earlier observations of this object made with the Hubble Space Telescope. A larger perturber with a mass of at least 104 M⊙ offers a more satisfactory explanation. Based on the lack of perturbation on other nearby sources in the same arc, the maximum mass of the perturber is 2.5 × 106 M⊙, making this the smallest substructure constrained by lensing at z > 0.3. The existence of this millilens is fully consistent with expectations from standard cold dark matter cosmology. On the other hand, the existence of such a small substructure in a cluster environment has implications for other dark matter models. In particular, warm dark matter models with particle masses below 8.7 keV are excluded by our observations. Similarly, axion dark matter models are consistent with the observations only if the axion mass is in the range 0.5 × 10−22 eV < ma < 5 × 10−22 eV.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".