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Record W4386721446 · doi:10.48550/arxiv.2309.05758

Zero Metallicity with Zero CPU Hours: Masses of the First Stars on the Laptop

2023· preprint· en· W4386721446 on OpenAlexfundno aff
James Gurian, Donghui Jeong, Boyuan Liu

Bibliographic record

VenuearXiv (Cornell University) · 2023
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Star Formation Studies
Canadian institutionsnot available
FundersMinistry of Colleges and UniversitiesInnovation, Science and Economic Development CanadaInstitut Périmètre de physique théoriqueNuclear Safety and Security CommissionGovernment of CanadaNational Aeronautics and Space Administration
KeywordsPhysicsAstrophysicsStarsMetallicityAccretion (finance)Radiative transferHaloStellar massAstronomyStar formationGalaxy

Abstract

fetched live from OpenAlex

We develop an analytic model for the mass of the first stars forming in the center of primordial gas clouds as a function of host halo mass, redshift, and degree of rotation. The model is based on the estimation of key timescales determining the following three processes: the collapse of the gas cloud, the accretion onto the protostellar core, and the radiative feedback of the protostellar core. The final stellar mass is determined by the total mass accreted until the radiative feedback halts the accretion. The analytic estimation, motivated by the result of the full numerical simulations, leads to algebraic expressions allowing an extremely fast execution. Despite its simplicity, the model reproduces the stellar mass scale and its parameter dependences observed in state-of-the-art cosmological zoom-in simulations. This work clarifies the basic physical principles undergirding such numerical treatments and provides a path to efficiently calibrating numerical predictions against eventual observations of the first stars.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.063
GPT teacher head0.182
Teacher spread0.119 · 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 designSimulation or modeling
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

Citations1
Published2023
Admission routes1
Has abstractyes

Explore more

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