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Record W2103605753 · doi:10.1093/mnras/sts156

Prospects for detecting the 21 cm forest from the diffuse intergalactic medium with LOFAR

2012· article· en· W2103605753 on OpenAlexaff
B. Ciardi, P. Labropoulos, Antonella Maselli, Rajat M. Thomas, Saleem Zaroubi, Luca Graziani, James S. Bolton, G. Bernardi, M. A. Brentjens, A. G. de Bruyn, S. Daiboo, G. Harker, Vibor Jelić, S. Kazemi, L. V. E. Koopmans, O. Martı́nez, Garrelt Mellema, A. R. Offringa, V. N. Pandey, Joop Schaye, V. Veligatla, H. K. Vedantham, S. Yatawatta

Bibliographic record

VenueMonthly Notices of the Royal Astronomical Society · 2012
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Cosmic Phenomena
Canadian institutionsUniversity of Toronto
FundersScience and Technology Facilities CouncilDeutsche ForschungsgemeinschaftNational Aeronautics and Space Administration
KeywordsReionizationPhysicsLOFARAstrophysicsRedshiftHydrogen lineLine-of-sightRadiative transferLyman-alpha forestAbsorption (acoustics)AstronomySpectral lineQuasarLine (geometry)Optical depthIntergalactic mediumRadio telescopeGalaxyOptics

Abstract

fetched live from OpenAlex

We discuss the feasibility of the detection of the 21 cm forest in the diffuse IGM with the radio telescope LOFAR. The optical depth to the 21 cm line has been derived using simulations of reionization which include detailed radiative transfer of ionizing photons. We find that the spectra from reionization models with similar total comoving hydrogen ionizing emissivity but different frequency distribution look remarkably similar. Thus, unless the reionization histories are very different from each other (e.g. a predominance ofUV vs. x-rayheating) we do not expect to distinguish them by means of observations of the 21 cm forest. Because the presence of a strong x-ray background would make the detection of 21 cm line absorption impossible, the lack of absorption could be used as a probe of the presence/intensity of the x-ray background and the

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.108
Threshold uncertainty score0.457

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.007
GPT teacher head0.194
Teacher spread0.187 · 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

Citations34
Published2012
Admission routes1
Has abstractyes

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