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Record W2082030684 · doi:10.1086/312652

Strong Lensing Reconstruction

2000· article· en· W2082030684 on OpenAlex

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.

Bibliographic record

VenueThe Astrophysical Journal · 2000
Typearticle
Languageen
FieldEngineering
TopicOptical Polarization and Ellipsometry
Canadian institutionsCanadian Institute for Theoretical Astrophysics
Fundersnot available
KeywordsSmoothingWeak gravitational lensingObservableAlgorithmNoise (video)PhysicsOpticsMathematicsComputer scienceImage (mathematics)AstrophysicsArtificial intelligenceStatistics

Abstract

fetched live from OpenAlex

We present a general linear algorithm for measuring the surface mass density 1-kappa from the observable reduced shear g=gamma&solm0;&parl0;1-kappa&parr0; in the strong lensing regime. We show that in general, the observed polarization field can be decomposed into "electric" and "magnetic" components, which have independent and redundant solutions, but orthogonal noise properties. By combining these solutions, one can increase the signal-to-noise ratio by 2. The solutions allow dynamic optimization of signal and noise, both in real and Fourier space (using arbitrary smoothing windows). Boundary conditions have no effect on the reconstructions, apart from its effect on the signal-to-noise ratio. Many existing reconstruction techniques are recovered as special cases of this framework. The magnetic solution has the added benefit of yielding the global and local parity of the reconstruction in a single step.

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 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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.915
Threshold uncertainty score0.537

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)0.0000.000

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 head0.008
GPT teacher head0.198
Teacher spread0.190 · 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