Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
VenueThe Astrophysical Journal Letters · 2022
Typearticle
Languageen
FieldComputer Science
TopicOptical measurement and interference techniques
Canadian institutionsCanadian Institute for Theoretical AstrophysicsCanadian Institute for Advanced ResearchMcGill UniversityUniversity of TorontoPerimeter InstituteUniversity of Waterloo
FundersLos Alamos National LaboratoryAstrophysics DivisionOffice of International Science and EngineeringNational Key Research and Development Program of ChinaJapan Society for the Promotion of SciencePhysics Division, National Center for Theoretical SciencesNational Nuclear Security AdministrationToray Science FoundationInstitut Périmètre de physique théoriqueAgencia Nacional de Investigación y DesarrolloIstituto Nazionale di Fisica NucleareAcademy of FinlandMax-Planck-GesellschaftCentre National de la Recherche ScientifiqueMinistry of Education, IndiaNational Natural Science Foundation of ChinaNational Research Foundation of KoreaMinisterio de Ciencia, Innovación y UniversidadesNuclear Safety and Security CommissionGeneralitat ValencianaNational Science FoundationChina Postdoctoral Science FoundationInstituto de Astrofísica de AndalucíaNederlandse Organisatie voor Wetenschappelijk OnderzoekChinese Academy of SciencesRecruitment Program of Global ExpertsShanghai Jiao Tong UniversityAcademia SinicaJunta de AndalucíaUniversiteit van AmsterdamRadboud UniversiteitNational Research FoundationUniversity of ArizonaSmithsonian InstitutionInternational Max Planck Research School for Environmental, Cellular and Molecular MicrobiologyNatural Sciences and Engineering Research Council of CanadaMinistry of Education, Culture, Sports, Science and TechnologyJohn Templeton FoundationChina Scholarship CouncilEuropean Southern ObservatoryKorea Astronomy and Space Science InstituteUniversiteit LeidenAssociated UniversitiesSpace Telescope Science InstituteCompute CanadaSimons FoundationNational Center for Theoretical SciencesNational Institutes of Natural SciencesGovernment of CanadaVetenskapsrådetU.S. Department of EnergyEuropean CommissionLeverhulme TrustNational Radio Astronomy ObservatoryNational Astronomical Observatory of JapanDirección General de Asuntos del Personal Académico, Universidad Nacional Autónoma de MéxicoMinisterio de Ciencia e InnovaciónConsejo Nacional de Ciencia y TecnologíaConsejo Superior de Investigaciones CientíficasUniversity of ChicagoHarvard UniversityNational Aeronautics and Space AdministrationDepartment of Science and Technology, Ministry of Science and Technology, IndiaGordon and Betty Moore FoundationFlatiron Health
KeywordsInterferometryComputer scienceRemote sensingArtificial intelligencePhysicsGeologyOptics
Abstract
fetched live from OpenAlexAbstract Recent developments in very long baseline interferometry (VLBI) have made it possible for the Event Horizon Telescope (EHT) to resolve the innermost accretion flows of the largest supermassive black holes on the sky. The sparse nature of the EHT’s ( u , v )-coverage presents a challenge when attempting to resolve highly time-variable sources. We demonstrate that the changing ( u , v )-coverage of the EHT can contain regions of time over the course of a single observation that facilitate dynamical imaging. These optimal time regions typically have projected baseline distributions that are approximately angularly isotropic and radially homogeneous. We derive a metric of coverage quality based on baseline isotropy and density that is capable of ranking array configurations by their ability to produce accurate dynamical reconstructions. We compare this metric to existing metrics in the literature and investigate their utility by performing dynamical reconstructions on synthetic data from simulated EHT observations of sources with simple orbital variability. We then use these results to make recommendations for imaging the 2017 EHT Sgr A* data set.
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.
Full frame distilled prediction
Teacher imitationNot 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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.855
Threshold uncertainty score0.709
Codex and Gemma teacher scores by category
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.
Teacher spread0.234 · 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
