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Record W4416876843 · doi:10.1051/0004-6361/202556570

The <i>z</i> = 9.625 Cosmic Gems galaxy was a compact “blue monster” propelled by massive star clusters

2025· article· en· W4416876843 on OpenAlexaff
E. Vanzella, Matteo Messa, Angela Adamo, F. Loiacono, Masamune Oguri, Keren Sharon, Larry Bradley, P. Bergamini, Adélaïde Claeyssens, Maruša Bradač, A. Zanella, A. Bolamperti, F. Calura, Tiger Yu-Yang Hsiao, E. Zackrisson, Massimo Ricotti, L. Christensen, J. M. Diego, F. E. Bauer, Xinfeng Xu, S. Fujimoto, C. Grillo, M. Lombardi, P. Rosati, Tom Resseguier, Adi Zitrin, Arjan Bik, Johan Richard, Abdurrouf, Rachana Bhatawdekar, Dan Coe, Brenda Frye, Akio Inoue, Yolanda Jiménez-Teja, Colin Norman, Jane R. Rigby, M. Trenti, Takuya Hashimoto

Bibliographic record

VenueAstronomy and Astrophysics · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsUniversity of Toronto
FundersAgencia Estatal de InvestigaciónMinistero dell’Istruzione, dell’Università e della RicercaJapan Society for the Promotion of ScienceUnited States-Israel Binational Science FoundationEuropean CommissionAgencia Nacional de Investigación y DesarrolloMinisterio de Ciencia, Innovación y UniversidadesNational Aeronautics and Space AdministrationSpace Telescope Science InstituteInstituto de Astrofísica de AndalucíaVetenskapsrådetNational Science Foundation
KeywordsGalaxyStellar massStar clusterStar formationGalaxy clusterCOSMIC cancer databaseInitial mass functionCluster (spacecraft)Stellar evolution

Abstract

fetched live from OpenAlex

The recent discovery of five massive stellar clusters at z = 9.625 in the Cosmic Gems galaxy has raised the question about the formation mechanism of star clusters in the first 500 Myr after the Big Bang. We inferred the total stellar mass in clusters by normalizing and integrating the stellar cluster mass function (SCMF, d n ( M )/d M = n 0 M β ), assuming three different slopes ( β = −1.5, −2.0, and −2.5) and different lower-mass limits between 10 2 and 10 5 M ⊙ . We compared the total integrated cluster stellar mass to the stellar mass inferred from the counter image of the Cosmic Gems, which provides the best modestly magnified ( μ = 1.84 ± 0.05) representation of the entire galaxy. The delensed stellar mass of the Cosmic Gems galaxy was estimated as 3.5 −1.8 +3.3 × 10 7 M ⊙ , with an effective radius of R eff = 103 −15 +13 parsecs and a stellar surface mass density of Σ mass = 520 −225 +340 M ⊙ pc −2 . Accounting for normalization uncertainties – including different lensing magnification scenarios for the arc – a modified SCMF, combined with a significantly high star cluster formation efficiency (approaching 100%), appears to be a necessary condition to explain the relatively short formation timescale of both the star clusters and the counter image, without exceeding the galaxy’s stellar mass. By extrapolating the physical properties at the peak of the burst, we found that in its recent past (≲30 Myr) the Cosmic Gems galaxy likely experienced a specific star formation rate exceeding 25 Gyr −1 and luminosity approaching the “blue monster” regime ( M UV < −20). Our study provides insights into the extreme clustered nature of star formation in early galaxies and sheds light on the formation of bound star clusters that might survive to z = 0 as globular clusters older than 13 Gyr.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.004
GPT teacher head0.190
Teacher spread0.186 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2025
Admission routes1
Has abstractyes

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