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Record W2062299538 · doi:10.4171/jst/58

A multichannel scheme in smooth scattering theory

2013· article· en· W2062299538 on OpenAlexaff
Alexander Pushnitski, D. R. Yafaev

Bibliographic record

VenueJournal of Spectral Theory · 2013
Typearticle
Languageen
FieldMathematics
TopicSpectral Theory in Mathematical Physics
Canadian institutionsToronto Metropolitan University
FundersMenzies Centre for Australian Studies, King's College London, University of LondonUniversité de Rennes 1Agence Nationale de la RechercheKing's College London
KeywordsScheme (mathematics)ScatteringComputer sciencePhysicsMathematicsOpticsMathematical analysis

Abstract

fetched live from OpenAlex

In this paper we develop the scattering theory for a pair of self-adjoint operators \mbox{ A_{0}=A_{1}\oplus\dots \oplus A_{N} } and A=A_{1}+\dots +A_{N} under the assumption that all pair products A_{j}A_{k} with j\neq k satisfy certain regularity conditions. Roughly speaking, these conditions mean that the products A_{j}A_{k} , j\neq k , can be represented as integral operators with smooth kernels in the spectral representation of the operator A_{0} . We show that the absolutely continuous parts of the operators A_{0} and A are unitarily equivalent. This yields a smooth version of Ismagilov's theorem known earlier in the trace class framework. We also prove that the singular continuous spectrum of the operator A is empty and that its eigenvalues may accumulate only to "thresholds'' of the absolutely continuous spectra of the operators A_{j} . Our approach relies on a system of resolvent equations which can be considered as a generalization of Faddeev's equations for three particle quantum systems.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0010.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.034
GPT teacher head0.305
Teacher spread0.271 · 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 designTheoretical or conceptual
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

Citations4
Published2013
Admission routes1
Has abstractyes

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