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Record W2792601224 · doi:10.1364/josab.35.000842

Interplay of pulse bandwidth and spectral resolution in spectral-focusing CARS microscopy

2018· article· en· W2792601224 on OpenAlexafffund
R. A. Cole, Aaron D. Slepkov

Bibliographic record

VenueJournal of the Optical Society of America B · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSpectroscopy Techniques in Biomedical and Chemical Research
Canadian institutionsTrent University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsOpticsBandwidth (computing)MicroscopyResolution (logic)Spectral resolutionMaterials sciencePulse (music)PhysicsOptoelectronicsComputer scienceTelecommunicationsSpectral lineArtificial intelligence

Abstract

fetched live from OpenAlex

We present a theoretical analysis of the relationship among the spectral resolution, the degree of chirp-matching, and pulse bandwidth in coherent anti-Stokes Raman scattering microscopy implementations based on spectral focusing (SF-CARS). We find that, despite allowing better ultimate spectral resolution when chirp-matching is attained, the use of the broadest bandwidth pulses can significantly worsen the spectral resolution if the pulses are not chirp-matched. We demonstrate that the bandwidth of the detected anti-Stokes signal is minimized when the pump is twice as chirped as the Stokes, meaning that—contrary to our initial presumption—a narrow anti-Stokes bandwidth does not imply good spectral resolution. We present approximate expressions that relate the bandwidths of the pump, Stokes, and anti-Stokes pulses to the degree of chirp-matching and outline how these could be used to estimate the amount of glass needed to attain chirp-matching. These expressions also permit a useful estimate of the group delay dispersion from other optics in the pump or Stokes beam path. This work aims to clarify several technical aspects of SF-CARS microscopy and provides researchers with valuable information to consider when working with SF-CARS microscopy 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 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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.547

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 studies0.0000.001
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 head0.007
GPT teacher head0.315
Teacher spread0.309 · 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 designBench or experimental
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

Citations22
Published2018
Admission routes2
Has abstractyes

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Same venueJournal of the Optical Society of America BSame topicSpectroscopy Techniques in Biomedical and Chemical ResearchFrench-language works237,207