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Record W4387744105 · doi:10.3390/galaxies11050107

Reference Array and Design Consideration for the Next-Generation Event Horizon Telescope

2023· article· en· W4387744105 on OpenAlexaff
Sheperd S. Doeleman, John Barrett, Lindy Blackburn, Katherine L. Bouman, Avery E. Broderick, Ryan Chaves, Vincent L. Fish, Garret Fitzpatrick, Mark Freeman, Antonio Fuentes, José L. Gómez, Kari Haworth, Janice Houston, Sara Issaoun, Michael D. Johnson, Mark Kettenis, Laurent Loinard, Neil M. Nagar, Gopal Narayanan, Aaron Oppenheimer, Daniel C. M. Palumbo, Nimesh Patel, Dominic W. Pesce, Alexander W. Raymond, Freek Roelofs, Ranjani Srinivasan, Paul Tiede, Jonathan Weintroub, Maciek Wielgus

Bibliographic record

VenueGalaxies · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicRadio Astronomy Observations and Technology
Canadian institutionsPerimeter InstituteUniversity of Waterloo
FundersAgencia Nacional de Investigación y DesarrolloNational Science FoundationConsejo Nacional de Ciencia y TecnologíaGordon and Betty Moore FoundationDirección General de Asuntos del Personal Académico, Universidad Nacional Autónoma de MéxicoJohn Templeton FoundationHarvard University
KeywordsVery-long-baseline interferometryTelescopeRadio telescopeEvent (particle physics)Computer scienceInterferometrySupermassive black holeEvent horizonPhysicsBaseline (sea)Remote sensingSystems engineeringAerospace engineeringOpticsTelecommunicationsAstronomyEngineeringGalaxyAstrophysicsGeology

Abstract

fetched live from OpenAlex

We describe the process to design, architect, and implement a transformative enhancement of the Event Horizon Telescope (EHT). This program—the next-generation Event Horizon Telescope (ngEHT)—will form a networked global array of radio dishes capable of making high-fidelity real-time movies of supermassive black holes (SMBH) and their emanating jets. This builds upon the EHT principally by deploying additional modest-diameter dishes to optimized geographic locations to enhance the current global mm/submm wavelength Very Long Baseline Interferometric (VLBI) array, which has, to date, utilized mostly pre-existing radio telescopes. The ngEHT program further focuses on observing at three frequencies simultaneously for increased sensitivity and Fourier spatial frequency coverage. Here, the concept, science goals, design considerations, station siting, and instrument prototyping are discussed, and a preliminary reference array to be implemented in phases is described.

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)0.003
metaresearch head score (Gemma)0.003
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 score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.005

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 head0.110
GPT teacher head0.278
Teacher spread0.169 · 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

Citations66
Published2023
Admission routes1
Has abstractyes

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