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Thermal Light Cannot Be Represented as a Statistical Mixture of Single Pulses

2015· article· en· W1695017524 on OpenAlexafffund
Aurélia Chenu, Agata M. Brańczyk, Gregory D. Scholes, J. E. Sipe

Bibliographic record

VenuePhysical Review Letters · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSpectroscopy and Quantum Chemical Studies
Canadian institutionsPerimeter InstituteUniversity of Toronto
FundersForeign Affairs and International Trade CanadaSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNatural Sciences and Engineering Research Council of CanadaAir Force Office of Scientific ResearchDefense Advanced Research Projects AgencyOntario Ministry of Research, Innovation and ScienceGovernment of Canada
KeywordsThermalCorrelation function (quantum field theory)Function (biology)PhysicsOpticsSpace (punctuation)Order (exchange)CorrelationStatistical physicsComputational physicsQuantum mechanicsMathematicsThermodynamicsComputer science

Abstract

fetched live from OpenAlex

We ask whether or not thermal light can be represented as a mixture of single broadband coherent pulses. We find that it cannot. Such a mixture is simply not rich enough to mimic thermal light; indeed, it cannot even reproduce the first-order correlation function. We show that it is possible to construct a modified mixture of single coherent pulses that does yield the correct first-order correlation function at equal space points. However, as we then demonstrate, such a mixture cannot reproduce the second-order correlation function.

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)0.002
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: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.003
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.026
GPT teacher head0.317
Teacher spread0.291 · 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

Citations27
Published2015
Admission routes2
Has abstractyes

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