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Record W6957794462 · doi:10.60692/4868p-mn791

Photon-sparse microscopy: visible light imaging using infrared illumination

2015· article· en· W6957794462 on OpenAlexaff

Bibliographic record

VenueGreater South Information System · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicRandom lasers and scattering media
Canadian institutionsUniversity of Ottawa
FundersEngineering and Physical Sciences Research Council
KeywordsPhotonVisible spectrumInfraredWavelengthBeam (structure)SIGNAL (programming language)Light beam

Abstract

fetched live from OpenAlex

Conventional imaging systems rely upon illumination light that is scattered or transmitted by the object and subsequently imaged. Ghost-imaging systems based on parametric down-conversion use twin beams of position-correlated signal and idler photons. One beam illuminates an object while the image information is recovered from a second beam that has never interacted with the object. In this Letter, we report on a camera-based ghost imaging system where the correlated photons have significantly different wavelengths. Infrared photons at 1550 nm wavelength illuminate the object and are detected by an InGaAs/InP single-photon avalanche diode. The image data are recorded from the coincidently detected, position-correlated, visible photons at a wavelength of 460 nm using a highly efficient, low-noise, photon-counting camera. The efficient transfer of the image information from infrared illumination to visible detection wavelengths and the ability to count single photons allows the acquisition of an image while illuminating the object with an optical power density of only 100 pJ cm−2 s−1. This wavelength-transforming ghost-imaging technique has potential for the imaging of light-sensitive specimens or where covert operation is desired.

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 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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.741
Threshold uncertainty score0.579

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 communication0.0000.001
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 head0.025
GPT teacher head0.227
Teacher spread0.202 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2015
Admission routes1
Has abstractyes

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