MétaCan
Menu
Back to cohort
Record W2963891109 · doi:10.1088/1538-3873/ab5bfd

Data Analysis for Precision 21 cm Cosmology

2020· article· en· W2963891109 on OpenAlexafffund
Adrian Liu, J. Richard Shaw

Bibliographic record

VenuePublications of the Astronomical Society of the Pacific · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicRadio Astronomy Observations and Technology
Canadian institutionsNational Research Council CanadaHerzberg Institute of AstrophysicsUniversity of British ColumbiaMcGill University
FundersDivision of Astronomical SciencesNatural Sciences and Engineering Research Council of CanadaCanadian Institute for Advanced Research
KeywordsCosmologyPhysicsTimelineObservational cosmologyTelescopeAstronomyPipeline (software)Field (mathematics)Computer scienceAstrophysicsDark energyGeographyArchaeology

Abstract

fetched live from OpenAlex

The redshifted 21 cm line is an emerging tool in cosmology, in principle permitting three-dimensional surveys of our Universe that reach unprecedentedly large volumes, previously inaccessible length scales, and hitherto unexplored epochs of our cosmic timeline. Large radio telescopes have been constructed for this purpose, and in recent years there has been considerable progress in transforming 21 cm cosmology from a field of considerable theoretical promise to one of observational reality. Increasingly, practitioners in the field are coming to the realization that the success of observational 21cm cosmology will hinge on software algorithms and analysis pipelines just as much as it does on careful hardware design and telescope construction. This review provides a pedagogical introduction to state-of-the-art ideas in 21 cm data analysis, covering a wide variety of steps in a typical analysis pipeline, from calibration to foreground subtraction to mapmaking to power spectrum estimation to parameter estimation.

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)0.011
metaresearch head score (Gemma)0.047
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: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.014
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.047
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0080.010
Science and technology studies0.0010.002
Scholarly communication0.0060.006
Open science0.0020.004
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0140.010

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 head0.047
GPT teacher head0.264
Teacher spread0.218 · 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
GenreMethods

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

Citations220
Published2020
Admission routes2
Has abstractyes

Explore more

Same venuePublications of the Astronomical Society of the PacificSame topicRadio Astronomy Observations and TechnologyFrench-language works237,207