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Record W4407925016 · doi:10.3847/1538-4357/adaf8d

A Very Long Baseline Interferometry Calibrator Grid at 600 MHz for Fast Radio Transient Localizations with CHIME/FRB Outriggers

2025· article· en· W4407925016 on OpenAlexaboutno aff
Shion Andrew, Calvin Leung, Alexander Li, Kiyoshi W. Masui, Bridget C. Andersen, Kevin Bandura, Alice P. Curtin, Jane Kaczmarek, Adam Lanman, Mattias Lazda, Juan Mena-Parra, Daniele Michilli, Kenzie Nimmo, Aaron B. Pearlman, Mubdi Rahman, Vishwangi Shah, Kaitlyn Shin, Haochen Wang

Bibliographic record

VenueThe Astrophysical Journal · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicRadio Astronomy Observations and Technology
Canadian institutionsnot available
Fundersnot available
KeywordsPhysicsInterferometryTransient (computer programming)AstrophysicsRadio telescopeAstronomy

Abstract

fetched live from OpenAlex

Abstract The Canadian Hydrogen Intensity Mapping Experiment Fast Radio Burst (CHIME/FRB) Project has a new very long baseline interferometry (VLBI) Outrigger at the Green Bank Observatory (GBO), which forms a 3300 km baseline with CHIME operating at 400–800 MHz. Using 100 ms long full-array baseband “snapshots” collected commensally during FRB and pulsar triggers, we perform a shallow, wide-area VLBI survey covering a significant fraction of the northern sky targeted at the positions of compact sources from the Radio Fundamental Catalog. In addition, our survey contains calibrators detected from two 1 s long trial baseband snapshots for a deeper survey with CHIME and GBO. In this paper, we present the largest catalogue of compact calibrators suitable for 30 mas scale VLBI observations at subgigahertz frequencies to date. Our catalogue consists of 200 total calibrators in the Northern Hemisphere that are compact on 30 mas scales with fluxes above 100 mJy. This calibrator grid will enable the precise localization of hundreds of FRBs a year with CHIME/FRB Outriggers.

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: none
Teacher disagreement score0.621
Threshold uncertainty score0.692

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.006
GPT teacher head0.218
Teacher spread0.212 · 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

Citations6
Published2025
Admission routes1
Has abstractyes

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