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Record W3203335179 · doi:10.82308/47066

Mapping the radio sky with CHIME using spherical harmonic imaging

2020· article· en· W3203335179 on OpenAlexaboutno aff
Paula Boubel

Bibliographic record

VenueOpen MIND · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicRadio Astronomy Observations and Technology
Canadian institutionsnot available
Fundersnot available
KeywordsSkyRemote sensingGeologyAstronomyGeographyPhysicsComputer science

Abstract

fetched live from OpenAlex

When the Universe was around 10 billion years old, it became dominated by dark energy andbegan to accelerate in its expansion. This stage in the expansion history of the Universe iscrucial for distinguishing dark energy models. The Canadian Hydrogen Intensity MappingExperiment (CHIME) is a radio telescope designed to measure the expansion during this periodby mapping the large-scale distribution of neutral hydrogen gas. CHIME will directly detect thehydrogen 21 cm emission redshifted to frequencies between 400 and 800 MHz. Astrophysicalforegrounds are several orders of magnitude brighter than the 21 cm cosmological signal.There is an ongoing effort to understand the instrument to the level of precision required forforeground removal. Measurements of the foregrounds are useful ancillary data products in and of themselves. Todate, the best measurement of the full sky in this frequency regime is the 408 MHz map byHaslam et al. from a survey that was conducted in the 70's. As a drift-scan interferometer,CHIME is uniquely capable of producing maps of the full radio sky on a daily basis. Thesedetailed maps provide valuable astrophysical data at unexplored frequencies. In this work, I willdescribe m-mode formalism, a non-traditional mapmaking strategy in which we take advantage of the instrument's drift-scan design. To validate the method, I show the result of applying it to simulated data. I will present the first CHIME all-sky map at 702 MHz. I discuss the sources of map artifacts and describe the steps that are being taken to mitigate them in forthcoming maps

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.784
Threshold uncertainty score0.999

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 science0.0000.000
Research integrity0.0000.000
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 head0.054
GPT teacher head0.260
Teacher spread0.205 · 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.

Study designNot applicable
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

Citations0
Published2020
Admission routes1
Has abstractyes

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