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Record W4399965180 · doi:10.1364/oe.530991

Broadband rapid-scanning phase-modulated Fourier transform electronic spectroscopy

2024· article· en· W4399965180 on OpenAlexaff
Ariba Javed, Julian Lüttig, Stephanie E. Sanders, Francesco Sessa, Alastair T. Gardiner, M. Joffre, Jennifer P. Ogilvie

Bibliographic record

VenueOptics Express · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSpectroscopy and Quantum Chemical Studies
Canadian institutionsUniversity of Ottawa
FundersAir Force Office of Scientific ResearchOffice of ScienceDirectorate for Mathematical and Physical SciencesHuman Frontier Science Program
KeywordsOpticsFourier transform spectroscopyMaterials scienceInterferometryFourier transformChirpPhase modulationSpectroscopyLaserFrequency modulationPhysicsFourier transform infrared spectroscopyBandwidth (computing)Phase noiseTelecommunicationsComputer science

Abstract

fetched live from OpenAlex

We present a phase-modulated approach for ultrabroadband Fourier transform electronic spectroscopy. To overcome the bandwidth limitations and spatial chirp introduced by acousto-optic modulators (AOMs), pulses from a 1 µm laser are modulated using AOMs prior to continuum generation. This phase modulation is transferred to the continuum generated in a yttrium aluminum garnet crystal. Separately generated phase-modulated continua in two arms of a Mach-Zehnder interferometer interfere with the difference of their modulation frequencies, enabling physical under-sampling of the signal and the suppression of low-frequency noise. By interferometrically tracking the relative time delay of the continua, we perform continuous, rapid-scanning Fourier transform electronic spectroscopy with a high signal-to-noise ratio and spectral resolution. As proof of principle, we measure the linear absorption and fluorescence excitation spectra of a laser dye and various biological samples.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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 science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.010
GPT teacher head0.287
Teacher spread0.276 · 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 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

Citations4
Published2024
Admission routes1
Has abstractyes

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