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Record W2171554667 · doi:10.1088/0004-637x/695/2/1430

THE INHOMOGENEOUS BACKGROUND OF H<sub>2</sub>-DISSOCIATING RADIATION DURING COSMIC REIONIZATION

2009· article· en· W2171554667 on OpenAlexaff

Bibliographic record

VenueThe Astrophysical Journal · 2009
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsCanadian Institute for Theoretical AstrophysicsUniversity of Toronto
Fundersnot available
KeywordsReionizationBackground radiationGalaxyRadiative transferStarlightRadiationStarsDark Ages

Abstract

fetched live from OpenAlex

The first, self-consistent calculations are presented of the cosmological, H2-dissociating UV background produced during the epoch of reionization by the sources of reionization. Large-scale radiative transfer simulations of reionization trace the impact of all the ionizing starlight on the IGM from all the sources in our simulation volume down to dwarf galaxies of mass ~10^8 Msun, identified by very high-resolution N-body simulations, including the self-regulating effect of IGM photoheating on dwarf galaxy formation. The UV continuum emitted below 13.6 eV by each source is then transferred through the same IGM, attenuated by atomic H Lyman series resonance lines, to predict the evolution of the inhomogeneous radiation background in the Lyman-Werner bands of H2 between 11 and 13.6 eV. On average, the intensity of this Lyman-Werner background is found to rise to the threshold level at which dissociation suppresses H2 cooling and star formation inside minihalos, long before reionization is complete. Spatial variations in the Lyman-Werner background are found which result from the clustering of sources associated with large-scale structure formation, such that intensity fluctuations correlate with matter density fluctuations. As a result, the Lyman-Werner background rises to the threshold level for H2 suppression earlier in the vicinity of the reionization sources and their H II regions.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.652
Threshold uncertainty score0.897

Codex and Gemma teacher scores by category

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

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 head0.005
GPT teacher head0.201
Teacher spread0.196 · 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 teacher head, 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

Citations132
Published2009
Admission routes1
Has abstractyes

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