Constraining the Mass of the Emerging Galaxy Cluster SpARCS1049+56 at z = 1.71 with Infrared Weak Lensing
Bibliographic record
Abstract
Abstract In the hierarchical structure formation model of the universe, galaxy clusters are assembled through a series of mergers. Accordingly, it is expected that galaxy clusters in the early universe are actively forming and dynamically young. Located at a high redshift of z = 1.71, SpARCS1049+56 offers a unique look into the galaxy cluster formation process. This cluster has been shown to be rich in cluster galaxies and to have intense star formation. Its high redshift pushes a weak-lensing analysis beyond the regime of the optical spectrum into that of the infrared. Equipped with deep Hubble Space Telescope Wide Field Camera 3 UVIS and IR observations, we present a weak-lensing characterization of SpARCS1049+56. As few IR weak-lensing studies have been performed, we discuss the details of point-spread function modeling and galaxy shape measurement for an IR weak-lensing procedure and the systematics that come with the territory. It will be critical to understand these systematics in future weak-lensing studies in the IR with the next-generation space telescopes such as the James Webb Space Telescope, Euclid, and WFIRST. Through a careful analysis, the mass distribution of this young galaxy cluster is mapped and the convergence peak is detected at a 3.3σ level. The weak-lensing mass of the cluster is estimated to be 3.5 ± 1.2 × 1014 M ⊙ and is consistent with the mass derived from a mass–richness scaling relation. This mass is extreme for a cluster at such a high redshift and suggests that SpARCS1049+56 is rare in the standard ΛCDM universe.
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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 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".