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Record W2989175718 · doi:10.1093/mnras/stz3083

Long troughs in the Lyman-α forest below redshift 6 due to islands of neutral hydrogen

2019· article· en· W2989175718 on OpenAlexaff
Laura C. Keating, Lewis H Weinberger, Girish Kulkarni, Martin G. Haehnelt, Jonathan Chardin, Dominique Aubert

Bibliographic record

VenueMonthly Notices of the Royal Astronomical Society · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsCanadian Institute for Theoretical AstrophysicsUniversity of Toronto
FundersEuropean Research CouncilScience and Technology Facilities CouncilEngineering and Physical Sciences Research Council
KeywordsPhysicsReionizationRedshiftAstrophysicsTrough (economics)Radiative transferIntergalactic mediumIonizationLyman-alpha forestPopulationSpectral lineAstronomyGalaxyOpticsIon

Abstract

fetched live from OpenAlex

ABSTRACT A long (110 cMpc h−1) and deep absorption trough in the Ly α forest has been observed extending down to redshift 5.5 in the spectrum of ULAS J0148+0600. Although no Ly α transmission is detected, Ly β spikes are present which has led to claims that the gas along this trough must be ionized. Using high-resolution cosmological radiative transfer simulations in large volumes, we show that in a scenario where reionization ends late (z ∼ 5.2), our simulations can reproduce troughs as long as observed. In this model, we find that the troughs are caused by islands of neutral hydrogen. Small ionized holes within the neutral islands allow for the transmission of Ly β. We have also modelled the Ly α emitter population around the simulated troughs, and show that there is a deficit of Ly α emitters close to the trough as is observed.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
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 head0.005
GPT teacher head0.194
Teacher spread0.188 · 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

Citations174
Published2019
Admission routes1
Has abstractyes

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